├── README.md ├── data ├── demo_data │ └── original │ │ ├── 000000.bin │ │ └── 000001.bin ├── interpolation_data.py ├── nuscenes_testlist.txt ├── nuscenes_trainlist.txt ├── scene-split │ ├── scene-0001.txt │ ├── scene-0002.txt │ ├── scene-0003.txt │ ├── scene-0004.txt │ ├── scene-0005.txt │ ├── scene-0006.txt │ ├── scene-0007.txt │ ├── scene-0008.txt │ ├── scene-0009.txt │ ├── scene-0010.txt │ ├── scene-0011.txt │ ├── scene-0012.txt │ ├── scene-0013.txt │ ├── scene-0014.txt │ ├── scene-0015.txt │ ├── scene-0016.txt │ ├── scene-0017.txt │ ├── scene-0018.txt │ ├── scene-0019.txt │ ├── scene-0020.txt │ ├── scene-0021.txt │ ├── scene-0022.txt │ ├── scene-0023.txt │ ├── scene-0024.txt │ ├── scene-0025.txt │ ├── scene-0026.txt │ ├── scene-0027.txt │ ├── scene-0028.txt │ ├── scene-0029.txt │ ├── scene-0030.txt │ ├── scene-0031.txt │ ├── scene-0032.txt │ ├── scene-0033.txt │ ├── scene-0034.txt │ ├── scene-0035.txt │ ├── scene-0036.txt │ ├── scene-0038.txt │ ├── scene-0039.txt │ ├── scene-0041.txt │ ├── scene-0042.txt │ ├── scene-0043.txt │ ├── scene-0044.txt │ ├── scene-0045.txt │ ├── scene-0046.txt │ ├── scene-0047.txt │ ├── scene-0048.txt │ ├── scene-0049.txt │ ├── scene-0050.txt │ ├── scene-0051.txt │ ├── scene-0052.txt │ ├── scene-0053.txt │ ├── scene-0054.txt │ ├── scene-0055.txt │ ├── scene-0056.txt │ ├── scene-0057.txt │ ├── scene-0058.txt │ ├── scene-0059.txt │ ├── scene-0060.txt │ ├── scene-0061.txt │ ├── scene-0062.txt │ ├── scene-0063.txt │ ├── scene-0064.txt │ ├── scene-0065.txt │ ├── scene-0066.txt │ ├── scene-0067.txt │ ├── scene-0068.txt │ ├── scene-0069.txt │ ├── scene-0070.txt │ ├── scene-0071.txt │ ├── scene-0072.txt │ ├── scene-0073.txt │ ├── scene-0074.txt │ ├── scene-0075.txt │ ├── scene-0076.txt │ ├── scene-0092.txt │ ├── scene-0093.txt │ ├── scene-0094.txt │ ├── scene-0095.txt │ ├── scene-0096.txt │ ├── scene-0097.txt │ ├── scene-0098.txt │ ├── scene-0099.txt │ ├── scene-0100.txt │ ├── scene-0101.txt │ ├── scene-0102.txt │ ├── scene-0103.txt │ ├── scene-0104.txt │ ├── scene-0105.txt │ ├── scene-0106.txt │ ├── scene-0107.txt │ ├── scene-0108.txt │ ├── scene-0109.txt │ ├── scene-0110.txt │ ├── scene-0120.txt │ ├── scene-0121.txt │ ├── scene-0122.txt │ ├── scene-0123.txt │ ├── scene-0124.txt │ ├── scene-0125.txt │ ├── scene-0126.txt │ ├── scene-0127.txt │ ├── scene-0128.txt │ ├── scene-0129.txt │ ├── scene-0130.txt │ ├── scene-0131.txt │ ├── scene-0132.txt │ ├── scene-0133.txt │ ├── scene-0134.txt │ ├── scene-0135.txt │ ├── scene-0138.txt │ ├── scene-0139.txt │ ├── scene-0149.txt │ ├── scene-0150.txt │ ├── scene-0151.txt │ ├── scene-0152.txt │ ├── scene-0154.txt │ ├── scene-0155.txt │ ├── scene-0157.txt │ ├── scene-0158.txt │ ├── scene-0159.txt │ ├── scene-0160.txt │ ├── scene-0161.txt │ ├── scene-0162.txt │ ├── scene-0163.txt │ ├── scene-0164.txt │ ├── scene-0165.txt │ ├── scene-0166.txt │ ├── scene-0167.txt │ ├── scene-0168.txt │ ├── scene-0170.txt │ ├── scene-0171.txt │ ├── scene-0172.txt │ ├── scene-0173.txt │ ├── scene-0174.txt │ ├── scene-0175.txt │ ├── scene-0176.txt │ ├── scene-0177.txt │ ├── scene-0178.txt │ ├── scene-0179.txt │ ├── scene-0180.txt │ ├── scene-0181.txt │ ├── scene-0182.txt │ ├── scene-0183.txt │ ├── scene-0184.txt │ ├── scene-0185.txt │ ├── scene-0187.txt │ ├── scene-0188.txt │ ├── scene-0190.txt │ ├── scene-0191.txt │ ├── scene-0192.txt │ ├── scene-0193.txt │ ├── scene-0194.txt │ ├── scene-0195.txt │ ├── scene-0196.txt │ ├── scene-0199.txt │ ├── scene-0200.txt │ ├── scene-0202.txt │ ├── scene-0203.txt │ ├── scene-0204.txt │ ├── scene-0206.txt │ ├── scene-0207.txt │ ├── scene-0208.txt │ ├── scene-0209.txt │ ├── scene-0210.txt │ ├── scene-0211.txt │ ├── scene-0212.txt │ ├── scene-0213.txt │ ├── scene-0214.txt │ ├── scene-0218.txt │ ├── scene-0219.txt │ ├── scene-0220.txt │ ├── scene-0221.txt │ ├── scene-0222.txt │ ├── scene-0224.txt │ ├── scene-0225.txt │ ├── scene-0226.txt │ ├── scene-0227.txt │ ├── scene-0228.txt │ ├── scene-0229.txt │ ├── scene-0230.txt │ ├── scene-0231.txt │ ├── scene-0232.txt │ ├── scene-0233.txt │ ├── scene-0234.txt │ ├── scene-0235.txt │ ├── scene-0236.txt │ ├── scene-0237.txt │ ├── scene-0238.txt │ ├── scene-0239.txt │ ├── scene-0240.txt │ ├── scene-0241.txt │ ├── scene-0242.txt │ ├── scene-0243.txt │ ├── scene-0244.txt │ ├── scene-0245.txt │ ├── scene-0246.txt │ ├── scene-0247.txt │ ├── scene-0248.txt │ ├── scene-0249.txt │ ├── scene-0250.txt │ ├── scene-0251.txt │ ├── scene-0252.txt │ ├── scene-0253.txt │ ├── scene-0254.txt │ ├── scene-0255.txt │ ├── scene-0256.txt │ ├── scene-0257.txt │ ├── scene-0258.txt │ ├── scene-0259.txt │ ├── scene-0260.txt │ ├── scene-0261.txt │ ├── scene-0262.txt │ ├── scene-0263.txt │ ├── scene-0264.txt │ ├── scene-0268.txt │ ├── scene-0269.txt │ ├── scene-0270.txt │ ├── scene-0271.txt │ ├── scene-0272.txt │ ├── scene-0273.txt │ ├── scene-0274.txt │ ├── scene-0275.txt │ ├── scene-0276.txt │ ├── scene-0277.txt │ ├── scene-0278.txt │ ├── scene-0283.txt │ ├── scene-0284.txt │ ├── scene-0285.txt │ ├── scene-0286.txt │ ├── scene-0287.txt │ ├── scene-0288.txt │ ├── scene-0289.txt │ ├── scene-0290.txt │ ├── scene-0291.txt │ ├── scene-0292.txt │ ├── scene-0293.txt │ ├── scene-0294.txt │ ├── scene-0295.txt │ ├── scene-0296.txt │ ├── scene-0297.txt │ ├── scene-0298.txt │ ├── scene-0299.txt │ ├── scene-0300.txt │ ├── scene-0301.txt │ ├── scene-0302.txt │ ├── scene-0303.txt │ ├── scene-0304.txt │ ├── scene-0305.txt │ ├── scene-0306.txt │ ├── scene-0315.txt │ ├── scene-0316.txt │ ├── scene-0317.txt │ ├── scene-0318.txt │ ├── scene-0321.txt │ ├── scene-0323.txt │ ├── scene-0324.txt │ ├── scene-0328.txt │ ├── scene-0329.txt │ ├── scene-0330.txt │ ├── scene-0331.txt │ ├── scene-0332.txt │ ├── scene-0344.txt │ ├── scene-0345.txt │ ├── scene-0346.txt │ ├── scene-0347.txt │ ├── scene-0348.txt │ ├── scene-0349.txt │ ├── scene-0350.txt │ ├── scene-0351.txt │ ├── scene-0352.txt │ ├── scene-0353.txt │ ├── scene-0354.txt │ ├── scene-0355.txt │ ├── scene-0356.txt │ ├── scene-0357.txt │ ├── scene-0358.txt │ ├── scene-0359.txt │ ├── scene-0360.txt │ ├── scene-0361.txt │ ├── scene-0362.txt │ ├── scene-0363.txt │ ├── scene-0364.txt │ ├── scene-0365.txt │ ├── scene-0366.txt │ ├── scene-0367.txt │ ├── scene-0368.txt │ ├── scene-0369.txt │ ├── scene-0370.txt │ ├── scene-0371.txt │ ├── scene-0372.txt │ ├── scene-0373.txt │ ├── scene-0374.txt │ ├── scene-0375.txt │ ├── scene-0376.txt │ ├── scene-0377.txt │ ├── scene-0378.txt │ ├── scene-0379.txt │ ├── scene-0380.txt │ ├── scene-0381.txt │ ├── scene-0382.txt │ ├── scene-0383.txt │ ├── scene-0384.txt │ ├── scene-0385.txt │ ├── scene-0386.txt │ ├── scene-0388.txt │ ├── scene-0389.txt │ ├── scene-0390.txt │ ├── scene-0391.txt │ ├── scene-0392.txt │ ├── scene-0393.txt │ ├── scene-0394.txt │ ├── scene-0395.txt │ ├── scene-0396.txt │ ├── scene-0397.txt │ ├── scene-0398.txt │ ├── scene-0399.txt │ ├── scene-0400.txt │ ├── scene-0401.txt │ ├── scene-0402.txt │ ├── scene-0403.txt │ ├── scene-0405.txt │ ├── scene-0406.txt │ ├── scene-0407.txt │ ├── scene-0408.txt │ ├── scene-0410.txt │ ├── scene-0411.txt │ ├── scene-0412.txt │ ├── scene-0413.txt │ ├── scene-0414.txt │ ├── scene-0415.txt │ ├── scene-0416.txt │ ├── scene-0417.txt │ ├── scene-0418.txt │ ├── scene-0419.txt │ ├── scene-0420.txt │ ├── scene-0421.txt │ ├── scene-0422.txt │ ├── scene-0423.txt │ ├── scene-0424.txt │ ├── scene-0425.txt │ ├── scene-0426.txt │ ├── scene-0427.txt │ ├── scene-0428.txt │ ├── scene-0429.txt │ ├── scene-0430.txt │ ├── scene-0431.txt │ ├── scene-0432.txt │ ├── scene-0433.txt │ ├── scene-0434.txt │ ├── scene-0435.txt │ ├── scene-0436.txt │ ├── scene-0437.txt │ ├── scene-0438.txt │ ├── scene-0439.txt │ ├── scene-0440.txt │ ├── scene-0441.txt │ ├── scene-0442.txt │ ├── scene-0443.txt │ ├── scene-0444.txt │ ├── scene-0445.txt │ ├── scene-0446.txt │ ├── scene-0447.txt │ ├── scene-0448.txt │ ├── scene-0449.txt │ ├── scene-0450.txt │ ├── scene-0451.txt │ ├── scene-0452.txt │ ├── scene-0453.txt │ ├── scene-0454.txt │ ├── scene-0455.txt │ ├── scene-0456.txt │ ├── scene-0457.txt │ ├── scene-0458.txt │ ├── scene-0459.txt │ ├── scene-0461.txt │ ├── scene-0462.txt │ ├── scene-0463.txt │ ├── scene-0464.txt │ ├── scene-0465.txt │ ├── scene-0467.txt │ ├── scene-0468.txt │ ├── scene-0469.txt │ ├── scene-0471.txt │ ├── scene-0472.txt │ ├── scene-0474.txt │ ├── scene-0475.txt │ ├── scene-0476.txt │ ├── scene-0477.txt │ ├── scene-0478.txt │ ├── scene-0479.txt │ ├── scene-0480.txt │ ├── scene-0499.txt │ ├── scene-0500.txt │ ├── scene-0501.txt │ ├── scene-0502.txt │ ├── scene-0504.txt │ ├── scene-0505.txt │ ├── scene-0506.txt │ ├── scene-0507.txt │ ├── scene-0508.txt │ ├── scene-0509.txt │ ├── scene-0510.txt │ ├── scene-0511.txt │ ├── scene-0512.txt │ ├── scene-0513.txt │ ├── scene-0514.txt │ ├── scene-0515.txt │ ├── scene-0517.txt │ ├── scene-0518.txt │ ├── scene-0519.txt │ ├── scene-0520.txt │ ├── scene-0521.txt │ ├── scene-0522.txt │ ├── scene-0523.txt │ ├── scene-0524.txt │ ├── scene-0525.txt │ ├── scene-0526.txt │ ├── scene-0527.txt │ ├── scene-0528.txt │ ├── scene-0529.txt │ ├── scene-0530.txt │ ├── scene-0531.txt │ ├── scene-0532.txt │ ├── scene-0533.txt │ ├── scene-0534.txt │ ├── scene-0535.txt │ ├── scene-0536.txt │ ├── scene-0537.txt │ ├── scene-0538.txt │ ├── scene-0539.txt │ ├── scene-0541.txt │ ├── scene-0542.txt │ ├── scene-0543.txt │ ├── scene-0544.txt │ ├── scene-0545.txt │ ├── scene-0546.txt │ ├── scene-0552.txt │ ├── scene-0553.txt │ ├── scene-0554.txt │ ├── scene-0555.txt │ ├── scene-0556.txt │ ├── scene-0557.txt │ ├── scene-0558.txt │ ├── scene-0559.txt │ ├── scene-0560.txt │ ├── scene-0561.txt │ ├── scene-0562.txt │ ├── scene-0563.txt │ ├── scene-0564.txt │ ├── scene-0565.txt │ ├── scene-0566.txt │ ├── scene-0568.txt │ ├── scene-0570.txt │ ├── scene-0571.txt │ ├── scene-0572.txt │ ├── scene-0573.txt │ ├── scene-0574.txt │ ├── scene-0575.txt │ ├── scene-0576.txt │ ├── scene-0577.txt │ ├── scene-0578.txt │ ├── scene-0580.txt │ ├── scene-0582.txt │ ├── scene-0583.txt │ ├── scene-0584.txt │ ├── scene-0585.txt │ ├── scene-0586.txt │ ├── scene-0587.txt │ ├── scene-0588.txt │ ├── scene-0589.txt │ ├── scene-0590.txt │ ├── scene-0591.txt │ ├── scene-0592.txt │ ├── scene-0593.txt │ ├── scene-0594.txt │ ├── scene-0595.txt │ ├── scene-0596.txt │ ├── scene-0597.txt │ ├── scene-0598.txt │ ├── scene-0599.txt │ ├── scene-0600.txt │ ├── scene-0625.txt │ ├── scene-0626.txt │ ├── scene-0627.txt │ ├── scene-0629.txt │ ├── scene-0630.txt │ ├── scene-0632.txt │ ├── scene-0633.txt │ ├── scene-0634.txt │ ├── scene-0635.txt │ ├── scene-0636.txt │ ├── scene-0637.txt │ ├── scene-0638.txt │ ├── scene-0639.txt │ ├── scene-0640.txt │ ├── scene-0641.txt │ ├── scene-0642.txt │ ├── scene-0643.txt │ ├── scene-0644.txt │ ├── scene-0645.txt │ ├── scene-0646.txt │ ├── scene-0647.txt │ ├── scene-0648.txt │ ├── scene-0649.txt │ ├── scene-0650.txt │ ├── scene-0651.txt │ ├── scene-0652.txt │ ├── scene-0653.txt │ ├── scene-0654.txt │ ├── scene-0655.txt │ ├── scene-0656.txt │ ├── scene-0657.txt │ ├── scene-0658.txt │ ├── scene-0659.txt │ ├── scene-0660.txt │ ├── scene-0661.txt │ ├── scene-0662.txt │ ├── scene-0663.txt │ ├── scene-0664.txt │ ├── scene-0665.txt │ ├── scene-0666.txt │ ├── scene-0667.txt │ ├── scene-0668.txt │ ├── scene-0669.txt │ ├── scene-0670.txt │ ├── scene-0671.txt │ ├── scene-0672.txt │ ├── scene-0673.txt │ ├── scene-0674.txt │ ├── scene-0675.txt │ ├── scene-0676.txt │ ├── scene-0677.txt │ ├── scene-0678.txt │ ├── scene-0679.txt │ ├── scene-0681.txt │ ├── scene-0683.txt │ ├── scene-0684.txt │ ├── scene-0685.txt │ ├── scene-0686.txt │ ├── scene-0687.txt │ ├── scene-0688.txt │ ├── scene-0689.txt │ ├── scene-0695.txt │ ├── scene-0696.txt │ ├── scene-0697.txt │ ├── scene-0698.txt │ ├── scene-0700.txt │ ├── scene-0701.txt │ ├── scene-0703.txt │ ├── scene-0704.txt │ ├── scene-0705.txt │ ├── scene-0706.txt │ ├── scene-0707.txt │ ├── scene-0708.txt │ ├── scene-0709.txt │ ├── scene-0710.txt │ ├── scene-0711.txt │ ├── scene-0712.txt │ ├── scene-0713.txt │ ├── scene-0714.txt │ ├── scene-0715.txt │ ├── scene-0716.txt │ ├── scene-0717.txt │ ├── scene-0718.txt │ ├── scene-0719.txt │ ├── scene-0726.txt │ ├── scene-0727.txt │ ├── scene-0728.txt │ ├── scene-0730.txt │ ├── scene-0731.txt │ ├── scene-0733.txt │ ├── scene-0734.txt │ ├── scene-0735.txt │ ├── scene-0736.txt │ ├── scene-0737.txt │ ├── scene-0738.txt │ ├── scene-0739.txt │ ├── scene-0740.txt │ ├── scene-0741.txt │ ├── scene-0744.txt │ ├── scene-0746.txt │ ├── scene-0747.txt │ ├── scene-0749.txt │ ├── scene-0750.txt │ ├── scene-0751.txt │ ├── scene-0752.txt │ ├── scene-0757.txt │ ├── scene-0758.txt │ ├── scene-0759.txt │ ├── scene-0760.txt │ ├── scene-0761.txt │ ├── scene-0762.txt │ ├── scene-0763.txt │ ├── scene-0764.txt │ ├── scene-0765.txt │ ├── scene-0767.txt │ ├── scene-0768.txt │ ├── scene-0769.txt │ ├── scene-0770.txt │ ├── scene-0771.txt │ ├── scene-0775.txt │ ├── scene-0777.txt │ ├── scene-0778.txt │ ├── scene-0780.txt │ ├── scene-0781.txt │ ├── scene-0782.txt │ ├── scene-0783.txt │ ├── scene-0784.txt │ ├── scene-0786.txt │ ├── scene-0787.txt │ ├── scene-0789.txt │ ├── scene-0790.txt │ ├── scene-0791.txt │ ├── scene-0792.txt │ ├── scene-0794.txt │ ├── scene-0795.txt │ ├── scene-0796.txt │ ├── scene-0797.txt │ ├── scene-0798.txt │ ├── scene-0799.txt │ ├── scene-0800.txt │ ├── scene-0802.txt │ ├── scene-0803.txt │ ├── scene-0804.txt │ ├── scene-0805.txt │ ├── scene-0806.txt │ ├── scene-0808.txt │ ├── scene-0809.txt │ ├── scene-0810.txt │ ├── scene-0811.txt │ ├── scene-0812.txt │ ├── scene-0813.txt │ ├── scene-0815.txt │ ├── scene-0816.txt │ ├── scene-0817.txt │ ├── scene-0819.txt │ ├── scene-0820.txt │ ├── scene-0821.txt │ ├── scene-0822.txt │ ├── scene-0847.txt │ ├── scene-0848.txt │ ├── scene-0849.txt │ ├── scene-0850.txt │ ├── scene-0851.txt │ ├── scene-0852.txt │ ├── scene-0853.txt │ ├── scene-0854.txt │ ├── scene-0855.txt │ ├── scene-0856.txt │ ├── scene-0858.txt │ ├── scene-0860.txt │ ├── scene-0861.txt │ ├── scene-0862.txt │ ├── scene-0863.txt │ ├── scene-0864.txt │ ├── scene-0865.txt │ ├── scene-0866.txt │ ├── scene-0868.txt │ ├── scene-0869.txt │ ├── scene-0870.txt │ ├── scene-0871.txt │ ├── scene-0872.txt │ ├── scene-0873.txt │ ├── scene-0875.txt │ ├── scene-0876.txt │ ├── scene-0877.txt │ ├── scene-0878.txt │ ├── scene-0880.txt │ ├── scene-0882.txt │ ├── scene-0883.txt │ ├── scene-0884.txt │ ├── scene-0885.txt │ ├── scene-0886.txt │ ├── scene-0887.txt │ ├── scene-0888.txt │ ├── scene-0889.txt │ ├── scene-0890.txt │ ├── scene-0891.txt │ ├── scene-0892.txt │ ├── scene-0893.txt │ ├── scene-0894.txt │ ├── scene-0895.txt │ ├── scene-0896.txt │ ├── scene-0897.txt │ ├── scene-0898.txt │ ├── scene-0899.txt │ ├── scene-0900.txt │ ├── scene-0901.txt │ ├── scene-0902.txt │ ├── scene-0903.txt │ ├── scene-0904.txt │ ├── scene-0905.txt │ ├── scene-0906.txt │ ├── scene-0907.txt │ ├── scene-0908.txt │ ├── scene-0909.txt │ ├── scene-0910.txt │ ├── scene-0911.txt │ ├── scene-0912.txt │ ├── scene-0913.txt │ ├── scene-0914.txt │ ├── scene-0915.txt │ ├── scene-0916.txt │ ├── scene-0917.txt │ ├── scene-0919.txt │ ├── scene-0920.txt │ ├── scene-0921.txt │ ├── scene-0922.txt │ ├── scene-0923.txt │ ├── scene-0924.txt │ ├── scene-0925.txt │ ├── scene-0926.txt │ ├── scene-0927.txt │ ├── scene-0928.txt │ ├── scene-0929.txt │ ├── scene-0930.txt │ ├── scene-0931.txt │ ├── scene-0945.txt │ ├── scene-0947.txt │ ├── scene-0949.txt │ ├── scene-0952.txt │ ├── scene-0953.txt │ ├── scene-0955.txt │ ├── scene-0956.txt │ ├── scene-0957.txt │ ├── scene-0958.txt │ ├── scene-0959.txt │ ├── scene-0960.txt │ ├── scene-0961.txt │ ├── scene-0962.txt │ ├── scene-0963.txt │ ├── scene-0966.txt │ ├── scene-0967.txt │ ├── scene-0968.txt │ ├── scene-0969.txt │ ├── scene-0971.txt │ ├── scene-0972.txt │ ├── scene-0975.txt │ ├── scene-0976.txt │ ├── scene-0977.txt │ ├── scene-0978.txt │ ├── scene-0979.txt │ ├── scene-0980.txt │ ├── scene-0981.txt │ ├── scene-0982.txt │ ├── scene-0983.txt │ ├── scene-0984.txt │ ├── scene-0988.txt │ ├── scene-0989.txt │ ├── scene-0990.txt │ ├── scene-0991.txt │ ├── scene-0992.txt │ ├── scene-0994.txt │ ├── scene-0995.txt │ ├── scene-0996.txt │ ├── scene-0997.txt │ ├── scene-0998.txt │ ├── scene-0999.txt │ ├── scene-1000.txt │ ├── scene-1001.txt │ ├── scene-1002.txt │ ├── scene-1003.txt │ ├── scene-1004.txt │ ├── scene-1005.txt │ ├── scene-1006.txt │ ├── scene-1007.txt │ ├── scene-1008.txt │ ├── scene-1009.txt │ ├── scene-1010.txt │ ├── scene-1011.txt │ ├── scene-1012.txt │ ├── scene-1013.txt │ ├── scene-1014.txt │ ├── scene-1015.txt │ ├── scene-1016.txt │ ├── scene-1017.txt │ ├── scene-1018.txt │ ├── scene-1019.txt │ ├── scene-1020.txt │ ├── scene-1021.txt │ ├── scene-1022.txt │ ├── scene-1023.txt │ ├── scene-1024.txt │ ├── scene-1025.txt │ ├── scene-1044.txt │ ├── scene-1045.txt │ ├── scene-1046.txt │ ├── scene-1047.txt │ ├── scene-1048.txt │ ├── scene-1049.txt │ ├── scene-1050.txt │ ├── scene-1051.txt │ ├── scene-1052.txt │ ├── scene-1053.txt │ ├── scene-1054.txt │ ├── scene-1055.txt │ ├── scene-1056.txt │ ├── scene-1057.txt │ ├── scene-1058.txt │ ├── scene-1059.txt │ ├── scene-1060.txt │ ├── scene-1061.txt │ ├── scene-1062.txt │ ├── scene-1063.txt │ ├── scene-1064.txt │ ├── scene-1065.txt │ ├── scene-1066.txt │ ├── scene-1067.txt │ ├── scene-1068.txt │ ├── scene-1069.txt │ ├── scene-1070.txt │ ├── scene-1071.txt │ ├── scene-1072.txt │ ├── scene-1073.txt │ ├── scene-1074.txt │ ├── scene-1075.txt │ ├── scene-1076.txt │ ├── scene-1077.txt │ ├── scene-1078.txt │ ├── scene-1079.txt │ ├── scene-1080.txt │ ├── scene-1081.txt │ ├── scene-1082.txt │ ├── scene-1083.txt │ ├── scene-1084.txt │ ├── scene-1085.txt │ ├── scene-1086.txt │ ├── scene-1087.txt │ ├── scene-1088.txt │ ├── scene-1089.txt │ ├── scene-1090.txt │ ├── scene-1091.txt │ ├── scene-1092.txt │ ├── scene-1093.txt │ ├── scene-1094.txt │ ├── scene-1095.txt │ ├── scene-1096.txt │ ├── scene-1097.txt │ ├── scene-1098.txt │ ├── scene-1099.txt │ ├── scene-1100.txt │ ├── scene-1101.txt │ ├── scene-1102.txt │ ├── scene-1104.txt │ ├── scene-1105.txt │ ├── scene-1106.txt │ ├── scene-1107.txt │ ├── scene-1108.txt │ ├── scene-1109.txt │ └── scene-1110.txt └── sceneflow_data.py ├── demo.py ├── figs └── interpolation.png ├── models ├── layers.py ├── models.py └── utils.py ├── pretrain_model ├── flownet3d_kitti_odometry_maxbias1.pth └── interp_kitti.pth ├── test.py ├── train_interp.py └── train_sceneflow.py /README.md: -------------------------------------------------------------------------------- 1 | # PointINet: Point Cloud Frame Interpolation Network 2 | 3 | ## Introduction 4 | 5 | The repository contains the source code and pre-trained models of our paper (published on AAAI 2021): `PointINet: Point Cloud Frame Interpolation Network`. 6 | 7 |
8 | 9 |
10 | 11 | ## Environment 12 | 13 | Our code is developed and tested on the following environment: 14 | 15 | - Python 3.6 16 | - PyTorch 1.4.0 17 | - Cuda 10.1 18 | - Numpy 1.19 19 | 20 | We utilized several open source library to implement the code: 21 | 22 | - [kaolin](https://github.com/NVIDIAGameWorks/kaolin) 23 | - [pytorch3d](https://github.com/facebookresearch/pytorch3d) 24 | - [PyTorchEMD](https://github.com/daerduoCarey/PyTorchEMD) (only for evaluation) 25 | - [Mayavi](https://docs.enthought.com/mayavi/mayavi/) (only for visualization of demo) 26 | - [wandb](https://app.wandb.ai/) (to record training process) 27 | 28 | ## Usage 29 | 30 | ### Dataset 31 | 32 | We utilize two large scale outdoor LiDAR dataset: 33 | 34 | - [Kitti odometry dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) 35 | - [nuScenes dataset](https://www.nuscenes.org/) 36 | 37 | To facilitate the implementation, we split the LiDAR point clouds in nuScenes dataset by scenes and the results are saved in `data/scene-split`. Besides, all of the LiDAR files in nuScenes dataset are stored in one single folder (include sweeps and samples). 38 | 39 | For the pre-training of FlowNet3D, please refer to [FlowNet3D](https://github.com/xingyul/flownet3d) to download the pre-processed dataset (Flythings3D and Kitti scene flow dataset). 40 | 41 | ### Demo 42 | 43 | We provide a demo to visualize the result, please run 44 | 45 | ```bash 46 | python demo.py --is_save IS_SAVE --visualize VISUALIZE 47 | ``` 48 | 49 | ### Training 50 | 51 | #### Training of FlowNet3D 52 | 53 | To train FlowNet3D, firstly train on Flythings3D dataset 54 | 55 | ```bash 56 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset Flythings3D --root DATAROOT --save_dir CHECKPOINTS_SAVE_DIR --train_type init 57 | ``` 58 | 59 | Then train it on Kitti scene flow dataset 60 | 61 | ```bash 62 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset Kitti --root DATAROOT --pretrain_model PRETRAIN_MODEL --save_dir CHECKPOINTS_SAVE_DIR --train_type init 63 | ``` 64 | 65 | After that train it on Kitti odometry dataset based on the model pretrained on Kitti scene flow dataset. 66 | 67 | ```bash 68 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset Kitti --root DATAROOT --pretrain_model PRETRAIN_MODEL --save_dir CHECKPOINTS_SAVE_DIR --train_type refine 69 | ``` 70 | 71 | Also train it on nuScenes dataset based on the model pretrained on Kitti scene flow dataset. 72 | 73 | ```bash 74 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset nuscenes --root DATAROOT --pretrain_model PRETRAIN_MODEL --save_dir CHECKPOINTS_SAVE_DIR --train_type refine 75 | ``` 76 | 77 | #### Training of PointINet 78 | 79 | We only train the PointINet on Kitti odometry dataset, run 80 | 81 | ```bash 82 | python train_interp.py --batch_size BATCH_SIZE --gpu GPU --dataset kitti --root DATAROOT --pretrain_model FLOWNET3D_PRETRAIN_MODEL --freeze 1 83 | ``` 84 | 85 | ### Testing 86 | 87 | To test on Kitti odometry dataset, run 88 | 89 | ```bash 90 | python test.py --gpu GPU --dataset kitti --root DATAROOT --pretrain_model POINTINET_PRETRAIN_MODEL --pretrain_flow_model FLOWNET3D_PRETRAIN_MODEL 91 | ``` 92 | 93 | To test on nuScenes dataset, run 94 | 95 | ```bash 96 | python test.py --gpu GPU --dataset nuscenes --root DATAROOT --pretrain_model POINTINET_PRETRAIN_MODEL --pretrain_flow_model FLOWNET3D_PRETRAIN_MODEL --scenelist TEST_SCENE_LIST 97 | ``` 98 | 99 | ## Citation 100 | 101 | @InProceedings{Lu2020_PointINet, 102 | author = {Lu, Fan and Chen, Guang and Qu, Sanqing and Li, Zhijun and Liu, Yinlong and Knoll, Alois}, 103 | title = {PointINet: Point Cloud Frame Interpolation Network}, 104 | booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, 105 | year = {2021} 106 | } -------------------------------------------------------------------------------- /data/demo_data/original/000000.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/data/demo_data/original/000000.bin -------------------------------------------------------------------------------- /data/demo_data/original/000001.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/data/demo_data/original/000001.bin -------------------------------------------------------------------------------- /data/interpolation_data.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.utils.data import Dataset 4 | from torch.utils.data import DataLoader 5 | import glob 6 | import numpy as np 7 | import os 8 | from tqdm import tqdm 9 | 10 | class KittiInterpolationDataset(Dataset): 11 | def __init__(self, root, npoints, interval, train=True, use_intensity=True): 12 | super(KittiInterpolationDataset, self).__init__() 13 | self.root = root 14 | self.npoints = npoints 15 | self.dataroot = os.path.join(self.root, 'velodyne') 16 | self.timeroot = os.path.join(self.root, 'times.txt') 17 | self.train = train 18 | self.use_intensity = use_intensity 19 | self.times = [] 20 | self.read_times() 21 | self.datapath = glob.glob(os.path.join(self.dataroot, '*.bin')) 22 | self.datapath = sorted(self.datapath) 23 | self.interval = interval 24 | self.dataset = self.make_dataset() 25 | 26 | def read_times(self): 27 | with open(self.timeroot) as f: 28 | for line in f.readlines(): 29 | line = line.strip() 30 | time = float(line) 31 | self.times.append(time) 32 | 33 | def get_cloud(self, file_name): 34 | pc = np.fromfile(file_name, dtype=np.float32, count=-1).reshape([-1,4]) 35 | return pc 36 | 37 | def make_dataset(self): 38 | max_ind = len(self.datapath) 39 | ini_index = 0 40 | end_index = 0 41 | index_lists = [] 42 | while (ini_index < max_ind - self.interval): 43 | end_index = ini_index + self.interval 44 | if self.train: 45 | mid_index = np.random.randint(1,self.interval) + ini_index 46 | triple = [ini_index, mid_index, end_index] 47 | index_lists.append(triple) 48 | else: 49 | for bias in range(1, self.interval): 50 | mid_index = bias + ini_index 51 | triple = [ini_index, mid_index, end_index] 52 | index_lists.append(triple) 53 | ini_index = end_index 54 | return index_lists 55 | 56 | def __getitem__(self, index): 57 | ini_index, mid_index, end_index = self.dataset[index] 58 | ini_pc = self.get_cloud(os.path.join(self.dataroot, self.datapath[ini_index])) 59 | mid_pc = self.get_cloud(os.path.join(self.dataroot, self.datapath[mid_index])) 60 | end_pc = self.get_cloud(os.path.join(self.dataroot, self.datapath[end_index])) 61 | 62 | ini_n = ini_pc.shape[0] 63 | mid_n = mid_pc.shape[0] 64 | end_n = end_pc.shape[0] 65 | 66 | if ini_n >= self.npoints: 67 | sample_idx_ini = np.random.choice(ini_n, self.npoints, replace=False) 68 | else: 69 | sample_idx_ini = np.concatenate((np.arange(ini_n), np.random.choice(ini_n, self.npoints - ini_n, replace=True)), axis=-1) 70 | if mid_n >= self.npoints: 71 | sample_idx_mid = np.random.choice(mid_n, self.npoints, replace=False) 72 | else: 73 | sample_idx_mid = np.concatenate((np.arange(mid_n), np.random.choice(mid_n, self.npoints - mid_n, replace=True)), axis=-1) 74 | if end_n >= self.npoints: 75 | sample_idx_end = np.random.choice(end_n, self.npoints, replace=False) 76 | else: 77 | sample_idx_end = np.concatenate((np.arange(end_n), np.random.choice(end_n, self.npoints - end_n, replace=True)), axis=-1) 78 | 79 | if self.use_intensity: 80 | ini_pc = ini_pc[sample_idx_ini, :].astype('float32') 81 | mid_pc = mid_pc[sample_idx_mid, :].astype('float32') 82 | end_pc = end_pc[sample_idx_end, :].astype('float32') 83 | else: 84 | ini_pc = ini_pc[sample_idx_ini, :3].astype('float32') 85 | mid_pc = mid_pc[sample_idx_mid, :3].astype('float32') 86 | end_pc = end_pc[sample_idx_end, :3].astype('float32') 87 | 88 | ini_color = np.zeros([self.npoints,3]).astype('float32') 89 | mid_color = np.zeros([self.npoints,3]).astype('float32') 90 | end_color = np.zeros([self.npoints,3]).astype('float32') 91 | 92 | ini_pc = torch.from_numpy(ini_pc).t() 93 | mid_pc = torch.from_numpy(mid_pc).t() 94 | end_pc = torch.from_numpy(end_pc).t() 95 | 96 | ini_color = torch.from_numpy(ini_color).t() 97 | mid_color = torch.from_numpy(mid_color).t() 98 | end_color = torch.from_numpy(end_color).t() 99 | 100 | ini_t = self.times[ini_index] 101 | mid_t = self.times[mid_index] 102 | end_t = self.times[end_index] 103 | 104 | t = (mid_t-ini_t)/(end_t-ini_t) 105 | 106 | return ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t 107 | 108 | def __len__(self): 109 | return len(self.dataset) 110 | 111 | class NuscenesDataset(Dataset): 112 | def __init__(self, npoints, root, scenes_list, use_intensity, interval, train): 113 | self.npoints = npoints 114 | self.root = root 115 | self.train = train 116 | self.scenes = self.read_scene_list(scenes_list) 117 | self.times_list, self.fns_list = self.load_scene(self.scenes) 118 | self.interval = interval 119 | self.dataset_fns, self.dataset_times = self.make_dataset() 120 | self.use_intensity = use_intensity 121 | 122 | 123 | def read_scene_list(self, scenes_list): 124 | scenes = [] 125 | with open(scenes_list, 'r') as f: 126 | for line in f.readlines(): 127 | line = line.strip('\n') 128 | scenes.append(line) 129 | return scenes 130 | 131 | def load_scene(self, scenes): 132 | times_list = [] 133 | fns_list = [] 134 | 135 | for scene in scenes: 136 | scene_file = os.path.join('./data/scene-split', scene+'.txt') 137 | times = [] 138 | fns = [] 139 | with open(scene_file) as f: 140 | for line in f.readlines(): 141 | line = line.strip('\n').split(' ') 142 | fn = line[0] 143 | timestamp = float(line[1]) 144 | fns.append(fn) 145 | times.append(timestamp) 146 | times_list.append(times) 147 | fns_list.append(fns) 148 | return times_list, fns_list 149 | 150 | def make_dataset(self): 151 | fns_lists = [] 152 | times_lists = [] 153 | for i in range(len(self.times_list)): 154 | times = self.times_list[i] 155 | fns = self.fns_list[i] 156 | max_ind = len(times) 157 | ini_index = 0 158 | end_index = 0 159 | while (ini_index < max_ind - self.interval): 160 | end_index = ini_index + self.interval 161 | if self.train: 162 | bias = np.random.randint(1, self.interval) 163 | fns_lists.append([fns[ini_index], fns[ini_index+bias], fns[end_index]]) 164 | times_lists.append([times[ini_index], times[ini_index+bias], times[end_index]]) 165 | else: 166 | for bias in range(1, self.interval): 167 | fns_lists.append([fns[ini_index], fns[ini_index+bias], fns[end_index]]) 168 | times_lists.append([times[ini_index], times[ini_index+bias], times[end_index]]) 169 | ini_index = end_index 170 | return fns_lists, times_lists 171 | 172 | def get_lidar(self, fn): 173 | scan_in = np.fromfile(fn, np.float32).reshape(-1,5) 174 | scan = np.zeros((scan_in.shape[0],4),np.float32) 175 | scan = scan_in[:,:4] 176 | return scan 177 | 178 | def __getitem__(self, index): 179 | ini_pc = self.get_lidar(os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][0])) 180 | mid_pc = self.get_lidar(os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][1])) 181 | end_pc = self.get_lidar(os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][2])) 182 | 183 | ini_n = ini_pc.shape[0] 184 | mid_n = mid_pc.shape[0] 185 | end_n = end_pc.shape[0] 186 | 187 | if ini_n >= self.npoints: 188 | sample_idx_ini = np.random.choice(ini_n, self.npoints, replace=False) 189 | else: 190 | sample_idx_ini = np.concatenate((np.arange(ini_n), np.random.choice(ini_n, self.npoints - ini_n, replace=True)), axis=-1) 191 | if mid_n >= self.npoints: 192 | sample_idx_mid = np.random.choice(mid_n, self.npoints, replace=False) 193 | else: 194 | sample_idx_mid = np.concatenate((np.arange(mid_n), np.random.choice(mid_n, self.npoints - mid_n, replace=True)), axis=-1) 195 | if end_n >= self.npoints: 196 | sample_idx_end = np.random.choice(end_n, self.npoints, replace=False) 197 | else: 198 | sample_idx_end = np.concatenate((np.arange(end_n), np.random.choice(end_n, self.npoints - end_n, replace=True)), axis=-1) 199 | 200 | if self.use_intensity: 201 | ini_pc = ini_pc[sample_idx_ini, :].astype('float32') 202 | mid_pc = mid_pc[sample_idx_mid, :].astype('float32') 203 | end_pc = end_pc[sample_idx_end, :].astype('float32') 204 | else: 205 | ini_pc = ini_pc[sample_idx_ini, :3].astype('float32') 206 | mid_pc = mid_pc[sample_idx_mid, :3].astype('float32') 207 | end_pc = end_pc[sample_idx_end, :3].astype('float32') 208 | 209 | ini_color = np.zeros([self.npoints,3]).astype('float32') 210 | mid_color = np.zeros([self.npoints,3]).astype('float32') 211 | end_color = np.zeros([self.npoints,3]).astype('float32') 212 | 213 | ini_pc = torch.from_numpy(ini_pc).t() 214 | mid_pc = torch.from_numpy(mid_pc).t() 215 | end_pc = torch.from_numpy(end_pc).t() 216 | 217 | ini_color = torch.from_numpy(ini_color).t() 218 | mid_color = torch.from_numpy(mid_color).t() 219 | end_color = torch.from_numpy(end_color).t() 220 | 221 | ini_t = self.dataset_times[index][0] 222 | mid_t = self.dataset_times[index][1] 223 | end_t = self.dataset_times[index][2] 224 | 225 | t = (mid_t-ini_t)/(end_t-ini_t) 226 | 227 | return ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t 228 | 229 | def __len__(self): 230 | return len(self.dataset_fns) -------------------------------------------------------------------------------- /data/nuscenes_testlist.txt: -------------------------------------------------------------------------------- 1 | scene-0127 2 | scene-0128 3 | scene-0129 4 | scene-0130 5 | scene-0131 6 | scene-0132 7 | scene-0133 8 | scene-0134 9 | scene-0135 10 | scene-0138 11 | scene-0139 12 | scene-0149 13 | scene-0150 14 | scene-0151 15 | scene-0152 16 | scene-0154 17 | scene-0155 18 | scene-0157 19 | scene-0158 20 | scene-0159 21 | scene-0160 22 | scene-0161 23 | scene-0162 24 | scene-0163 25 | scene-0164 26 | scene-0165 27 | scene-0166 28 | scene-0167 29 | scene-0168 30 | scene-0170 31 | scene-0171 32 | scene-0172 33 | scene-0173 34 | scene-0174 35 | scene-0175 36 | scene-0176 37 | scene-0177 38 | scene-0178 39 | scene-0179 40 | scene-0180 41 | scene-0181 42 | scene-0182 43 | scene-0183 44 | scene-0184 45 | scene-0185 46 | scene-0187 47 | scene-0188 48 | scene-0190 49 | scene-0191 50 | scene-0192 51 | scene-0193 52 | scene-0194 53 | scene-0195 54 | scene-0196 55 | scene-0199 56 | scene-0200 57 | scene-0202 58 | scene-0203 59 | scene-0204 60 | scene-0206 61 | scene-0207 62 | scene-0208 63 | scene-0209 64 | scene-0210 65 | scene-0211 66 | scene-0212 67 | scene-0213 68 | scene-0214 69 | scene-0218 70 | scene-0219 71 | scene-0220 72 | scene-0221 73 | scene-0222 74 | scene-0224 75 | scene-0225 76 | scene-0226 77 | scene-0227 78 | scene-0228 79 | scene-0229 80 | scene-0230 81 | scene-0231 82 | scene-0232 83 | scene-0233 84 | scene-0234 85 | scene-0235 86 | scene-0236 87 | scene-0237 88 | scene-0238 89 | scene-0239 90 | scene-0240 91 | scene-0241 92 | scene-0242 93 | scene-0243 94 | scene-0244 95 | scene-0245 96 | scene-0246 97 | scene-0247 98 | scene-0248 99 | scene-0249 100 | scene-0250 101 | scene-0251 102 | scene-0252 103 | scene-0253 104 | scene-0254 105 | scene-0255 106 | scene-0256 107 | scene-0257 108 | scene-0258 109 | scene-0259 110 | scene-0260 111 | scene-0261 112 | scene-0262 113 | scene-0263 114 | scene-0264 115 | scene-0268 116 | scene-0269 117 | scene-0270 118 | scene-0271 119 | scene-0272 120 | scene-0273 121 | scene-0274 122 | scene-0275 123 | scene-0276 124 | scene-0277 125 | scene-0278 126 | scene-0283 127 | scene-0284 128 | scene-0285 129 | scene-0286 130 | scene-0287 131 | scene-0288 132 | scene-0289 133 | scene-0290 134 | scene-0291 135 | scene-0292 136 | 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n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708067203498.pcd.bin 1532708067203498.0 377 | -------------------------------------------------------------------------------- /data/sceneflow_data.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.utils.data import Dataset 4 | import glob 5 | import numpy as np 6 | import os 7 | from tqdm import tqdm 8 | 9 | class Flythings3D(Dataset): 10 | def __init__(self, npoints=2048, root='data/data_processed_maxcut_35_20k_2k_8192', train=True, cache=None): 11 | self.npoints = npoints 12 | self.train = train 13 | self.root = root 14 | if self.train: 15 | self.datapath = glob.glob(os.path.join(self.root, 'TRAIN*.npz')) 16 | else: 17 | self.datapath = glob.glob(os.path.join(self.root, 'TEST*.npz')) 18 | 19 | if cache is None: 20 | self.cache = {} 21 | else: 22 | self.cache = cache 23 | 24 | self.cache_size = 30000 25 | 26 | ###### deal with one bad datapoint with nan value 27 | self.datapath = [d for d in self.datapath if 'TRAIN_C_0140_left_0006-0' not in d] 28 | ###### 29 | 30 | def __getitem__(self, index): 31 | if index in self.cache: 32 | pos1, pos2, color1, color2, flow, mask1 = self.cache[index] 33 | else: 34 | fn = self.datapath[index] 35 | with open(fn, 'rb') as fp: 36 | data = np.load(fp) 37 | pos1 = data['points1'].astype('float32') 38 | pos2 = data['points2'].astype('float32') 39 | color1 = data['color1'].astype('float32') / 255 40 | color2 = data['color2'].astype('float32') / 255 41 | flow = data['flow'].astype('float32') 42 | mask1 = data['valid_mask1'] 43 | 44 | if len(self.cache) < self.cache_size: 45 | self.cache[index] = (pos1, pos2, color1, color2, flow, mask1) 46 | 47 | if self.train: 48 | n1 = pos1.shape[0] 49 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False) 50 | n2 = pos2.shape[0] 51 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False) 52 | 53 | pos1 = pos1[sample_idx1, :] 54 | pos2 = pos2[sample_idx2, :] 55 | color1 = color1[sample_idx1, :] 56 | color2 = color2[sample_idx2, :] 57 | flow = flow[sample_idx1, :] 58 | mask1 = mask1[sample_idx1] 59 | else: 60 | pos1 = pos1[:self.npoints, :] 61 | pos2 = pos2[:self.npoints, :] 62 | color1 = color1[:self.npoints, :] 63 | color2 = color2[:self.npoints, :] 64 | flow = flow[:self.npoints, :] 65 | mask1 = mask1[:self.npoints] 66 | 67 | pos1_center = np.mean(pos1, 0) 68 | pos1 -= pos1_center 69 | pos2 -= pos1_center 70 | 71 | pos1 = torch.from_numpy(pos1).t() 72 | pos2 = torch.from_numpy(pos2).t() 73 | color1 = torch.from_numpy(color1).t() 74 | color2 = torch.from_numpy(color2).t() 75 | flow = torch.from_numpy(flow).t() 76 | mask1 = torch.from_numpy(mask1) 77 | 78 | return pos1, pos2, color1, color2, flow, mask1 79 | 80 | def __len__(self): 81 | return len(self.datapath) 82 | 83 | class KittiSceneFlowDataset(Dataset): 84 | def __init__(self, root, npoints, train=True): 85 | self.npoints = npoints 86 | self.root = root 87 | self.datapath = glob.glob(os.path.join(self.root, '*.npz')) 88 | 89 | def __getitem__(self, index): 90 | fn = self.datapath[index] 91 | with open(fn, 'rb') as fp: 92 | data = np.load(fp) 93 | pos1 = data['pos1'].astype('float32') 94 | pos2 = data['pos2'].astype('float32') 95 | flow = data['gt'].astype('float32') 96 | 97 | n1 = pos1.shape[0] 98 | n2 = pos2.shape[0] 99 | 100 | if n1 >= self.npoints: 101 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False) 102 | else: 103 | sample_idx1 = np.concatenate((np.arange(n1), np.random.choice(n1, self.npoints - n1, replace=True)), axis=-1) 104 | if n2 >= self.npoints: 105 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False) 106 | else: 107 | sample_idx2 = np.concatenate((np.arange(n2), np.random.choice(n2, self.npoints - n2, replace=True)), axis=-1) 108 | 109 | pos1 = pos1[sample_idx1, :] 110 | pos2 = pos2[sample_idx2, :] 111 | flow = flow[sample_idx1, :] 112 | 113 | color1 = np.zeros([self.npoints,3], dtype=np.float32) 114 | color2 = np.zeros([self.npoints,3], dtype=np.float32) 115 | mask1 = np.ones([self.npoints]) 116 | 117 | pos1 = torch.from_numpy(pos1).t() 118 | pos2 = torch.from_numpy(pos2).t() 119 | color1 = torch.from_numpy(color1).t() 120 | color2 = torch.from_numpy(color2).t() 121 | flow = torch.from_numpy(flow).t() 122 | mask1 = torch.from_numpy(mask1) 123 | 124 | return pos1, pos2, color1, color2, flow, mask1 125 | 126 | def __len__(self): 127 | return len(self.datapath) 128 | 129 | class KittiOdometrySceneflow(Dataset): 130 | def __init__(self, root, npoints, max_bias, train=True): 131 | self.npoints = npoints 132 | self.root = root 133 | self.train = train 134 | self.max_bias = max_bias 135 | self.datapath = glob.glob(os.path.join(self.root, '*.bin')) 136 | self.datapath = sorted(self.datapath) 137 | 138 | def get_cloud(self, fn): 139 | pc = np.fromfile(fn, dtype=np.float32, count=-1).reshape([-1,4]) 140 | return pc 141 | 142 | def __getitem__(self, index): 143 | pc1_fn = os.path.join(self.root, self.datapath[index]) 144 | max_ind = len(self.datapath) 145 | if index <= self.max_bias: 146 | bias = np.random.randint(1, self.max_bias+1) 147 | elif index >= max_ind - self.max_bias: 148 | bias = np.random.randint(-self.max_bias,0) 149 | else: 150 | bias = np.random.randint(-self.max_bias,self.max_bias+1) 151 | if bias == 0: 152 | bias = bias + 1 153 | 154 | pc2_fn = os.path.join(self.root, self.datapath[index+bias]) 155 | 156 | points1 = self.get_cloud(pc1_fn) 157 | points2 = self.get_cloud(pc2_fn) 158 | 159 | n1 = points1.shape[0] 160 | n2 = points2.shape[0] 161 | 162 | if n1 >= self.npoints: 163 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False) 164 | else: 165 | sample_idx1 = np.concatenate((np.arange(n1), np.random.choice(n1, self.npoints - n1, replace=True)), axis=-1) 166 | if n2 >= self.npoints: 167 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False) 168 | else: 169 | sample_idx2 = np.concatenate((np.arange(n2), np.random.choice(n2, self.npoints - n2, replace=True)), axis=-1) 170 | 171 | points1 = points1[sample_idx1, :3].astype('float32') 172 | points2 = points2[sample_idx2, :3].astype('float32') 173 | color1 = np.zeros([self.npoints,3]).astype('float32') 174 | color2 = np.zeros([self.npoints,3]).astype('float32') 175 | 176 | points1 = torch.from_numpy(points1).t() 177 | points2 = torch.from_numpy(points2).t() 178 | color1 = torch.from_numpy(color1).t() 179 | color2 = torch.from_numpy(color2).t() 180 | 181 | return points1, points2, color1, color2 182 | 183 | def __len__(self): 184 | return len(self.datapath) 185 | 186 | class NuScenesFlow(Dataset): 187 | def __init__(self, root, npoints, scenes_list, max_bias): 188 | self.npoints = npoints 189 | self.root = root 190 | self.scenes = self.read_scene_list(scenes_list) 191 | self.times_list, self.fns_list = self.load_scene(self.scenes) 192 | self.max_bias = max_bias 193 | self.dataset_fns, self.dataset_times = self.make_dataset() 194 | 195 | def read_scene_list(self, scenes_list): 196 | scenes = [] 197 | with open(scenes_list, 'r') as f: 198 | for line in f.readlines(): 199 | line = line.strip('\n') 200 | scenes.append(line) 201 | return scenes 202 | 203 | def load_scene(self, scenes): 204 | times_list = [] 205 | fns_list = [] 206 | 207 | for scene in scenes: 208 | scene_file = os.path.join('./data/scene-split', scene+'.txt') 209 | times = [] 210 | fns = [] 211 | with open(scene_file) as f: 212 | for line in f.readlines(): 213 | line = line.strip('\n').split(' ') 214 | fn = line[0] 215 | timestamp = float(line[1]) 216 | fns.append(fn) 217 | times.append(timestamp) 218 | times_list.append(times) 219 | fns_list.append(fns) 220 | return times_list, fns_list 221 | 222 | def make_dataset(self): 223 | fns_lists = [] 224 | times_lists = [] 225 | for i in tqdm(range(len(self.times_list))): 226 | times = self.times_list[i] 227 | fns = self.fns_list[i] 228 | max_ind = len(times) 229 | ini_index = 0 230 | while (ini_index < max_ind - self.max_bias): 231 | if ini_index <= self.max_bias: 232 | bias = np.random.randint(1, self.max_bias+1) 233 | else: 234 | bias = np.random.randint(-self.max_bias,self.max_bias+1) 235 | if bias == 0: 236 | bias = bias + 1 237 | end_index = ini_index + bias 238 | fns_lists.append([fns[ini_index], fns[end_index]]) 239 | times_lists.append([times[ini_index], times[end_index]]) 240 | ini_index += 1 241 | return fns_lists, times_lists 242 | 243 | def get_lidar(self, fn): 244 | scan_in = np.fromfile(fn, np.float32).reshape(-1,5) 245 | scan = np.zeros((scan_in.shape[0],4),np.float32) 246 | scan = scan_in[:,:4] 247 | return scan 248 | 249 | def __getitem__(self, index): 250 | pc1_fn = os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][0]) 251 | pc2_fn = os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][1]) 252 | points1 = self.get_lidar(pc1_fn) 253 | points2 = self.get_lidar(pc2_fn) 254 | 255 | n1 = points1.shape[0] 256 | n2 = points2.shape[0] 257 | 258 | if n1 >= self.npoints: 259 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False) 260 | else: 261 | sample_idx1 = np.concatenate((np.arange(n1), np.random.choice(n1, self.npoints - n1, replace=True)), axis=-1) 262 | if n2 >= self.npoints: 263 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False) 264 | else: 265 | sample_idx2 = np.concatenate((np.arange(n2), np.random.choice(n2, self.npoints - n2, replace=True)), axis=-1) 266 | 267 | points1 = points1[sample_idx1, :3].astype('float32') 268 | points2 = points2[sample_idx2, :3].astype('float32') 269 | color1 = np.zeros([self.npoints,3]).astype('float32') 270 | color2 = np.zeros([self.npoints,3]).astype('float32') 271 | 272 | points1 = torch.from_numpy(points1).t() 273 | points2 = torch.from_numpy(points2).t() 274 | color1 = torch.from_numpy(color1).t() 275 | color2 = torch.from_numpy(color2).t() 276 | 277 | return points1, points2, color1, color2 278 | 279 | def __len__(self): 280 | return len(self.dataset_fns) -------------------------------------------------------------------------------- /demo.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import numpy as np 4 | 5 | from models.models import PointINet 6 | 7 | import mayavi.mlab as mlab 8 | 9 | import argparse 10 | from tqdm import tqdm 11 | import os 12 | 13 | def parse_args(): 14 | parser = argparse.ArgumentParser(description='demo') 15 | parser.add_argument('--gpu', type=str, default='0') 16 | parser.add_argument('--npoints', type=int, default=32768) 17 | parser.add_argument('--pretrain_model', type=str, default='./pretrain_model/interp_kitti.pth') 18 | parser.add_argument('--pretrain_flow_model', type=str, default='./pretrain_model/flownet3d_kitti_odometry_maxbias1.pth') 19 | parser.add_argument('--is_save', type=int, default=1) 20 | parser.add_argument('--visualize', type=int, default=1) 21 | 22 | return parser.parse_args() 23 | 24 | def get_lidar(fn, npoints): 25 | points = np.fromfile(fn, dtype=np.float32, count=-1).reshape([-1,4]) 26 | raw_num = points.shape[0] 27 | if raw_num >= npoints: 28 | sample_idx = np.random.choice(raw_num, npoints, replace=False) 29 | else: 30 | sample_idx = np.concatenate((np.arange(raw_num), np.random.choice(raw_num, npoints - raw_num, replace=True)), axis=-1) 31 | 32 | pc = points[sample_idx, :] 33 | pc = torch.from_numpy(pc).t() 34 | color = np.zeros([npoints,3]).astype('float32') 35 | color = torch.from_numpy(color).t() 36 | 37 | pc = pc.unsqueeze(0).cuda() 38 | color = color.unsqueeze(0).cuda() 39 | 40 | return pc, color 41 | 42 | def demo(args): 43 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu 44 | 45 | fn1 = './data/demo_data/original/000000.bin' 46 | fn2 = './data/demo_data/original/000001.bin' 47 | 48 | net = PointINet() 49 | net.load_state_dict(torch.load(args.pretrain_model)) 50 | net.flow.load_state_dict(torch.load(args.pretrain_flow_model)) 51 | net.eval() 52 | net.cuda() 53 | 54 | interp_scale = 5 55 | t_array = np.arange(1.0/interp_scale, 1.0, 1.0/interp_scale, dtype=np.float32) 56 | 57 | with torch.no_grad(): 58 | pc1, color1 = get_lidar(fn1, args.npoints) 59 | pc2, color2 = get_lidar(fn2, args.npoints) 60 | 61 | for i in range(interp_scale-1): 62 | t = t_array[i] 63 | t = torch.tensor([t]) 64 | t = t.cuda().float() 65 | 66 | pred_mid_pc = net(pc1, pc2, color1, color2, t) 67 | 68 | ini_pc = pc1.squeeze(0).permute(1,0).cpu().numpy() 69 | end_pc = pc2.squeeze(0).permute(1,0).cpu().numpy() 70 | 71 | 72 | pred_mid_pc = pred_mid_pc.squeeze(0).permute(1,0).cpu().numpy() 73 | 74 | if args.visualize == 1: 75 | fig = mlab.figure(figure=None, bgcolor=(1,1,1), fgcolor=(0,0,0), engine=None, size=(1600, 1000)) 76 | mlab.points3d(ini_pc[:,0],ini_pc[:,1],ini_pc[:,2],color=(0,0,1),scale_factor=0.2,figure=fig, mode='sphere') 77 | mlab.points3d(end_pc[:,0],end_pc[:,1],end_pc[:,2],color=(0,1,0),scale_factor=0.2,figure=fig, mode='sphere') 78 | mlab.points3d(pred_mid_pc[:,0],pred_mid_pc[:,1],pred_mid_pc[:,2],color=(1,0,0),scale_factor=0.2,figure=fig, mode='sphere') 79 | mlab.show() 80 | 81 | if args.is_save == 1: 82 | save_dir = './data/demo_data/interpolated' 83 | if not os.path.exists(save_dir): 84 | os.makedirs(save_dir) 85 | save_name = os.path.join(save_dir, str(t.squeeze().cpu().numpy())+'.bin') 86 | pred_mid_pc.tofile(save_name) 87 | print("save interpolated point clouds to:", save_name) 88 | 89 | if __name__ == '__main__': 90 | args = parse_args() 91 | demo(args) -------------------------------------------------------------------------------- /figs/interpolation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/figs/interpolation.png -------------------------------------------------------------------------------- /models/layers.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | 6 | # We utilize kaolin to implement layers for FlowNet3D 7 | # website: https://github.com/NVIDIAGameWorks/kaolin 8 | import kaolin as kal 9 | from kaolin.models.PointNet2 import furthest_point_sampling 10 | from kaolin.models.PointNet2 import fps_gather_by_index 11 | from kaolin.models.PointNet2 import ball_query 12 | from kaolin.models.PointNet2 import group_gather_by_index 13 | from kaolin.models.PointNet2 import three_nn 14 | from kaolin.models.PointNet2 import three_interpolate 15 | 16 | # We utilize pytorch3d to implement k-nearest-neighbor search 17 | # website: https://github.com/facebookresearch/pytorch3d 18 | from pytorch3d.ops import knn_points, knn_gather 19 | 20 | from .utils import pdist2squared 21 | 22 | ''' 23 | Layers for FlowNet3D 24 | ''' 25 | class Sample(nn.Module): 26 | ''' 27 | Furthest point sample 28 | ''' 29 | def __init__(self, num_points): 30 | super(Sample, self).__init__() 31 | 32 | self.num_points = num_points 33 | 34 | def forward(self, points): 35 | new_points_ind = furthest_point_sampling(points.permute(0, 2, 1).contiguous(), self.num_points) 36 | new_points = fps_gather_by_index(points, new_points_ind) 37 | return new_points 38 | 39 | class Group(nn.Module): 40 | ''' 41 | kNN group for FlowNet3D 42 | ''' 43 | def __init__(self, radius, num_samples, knn=False): 44 | super(Group, self).__init__() 45 | 46 | self.radius = radius 47 | self.num_samples = num_samples 48 | self.knn = knn 49 | 50 | def forward(self, points, new_points, features): 51 | if self.knn: 52 | dist = pdist2squared(points, new_points) 53 | ind = dist.topk(self.num_samples, dim=1, largest=False)[1].int().permute(0, 2, 1).contiguous() 54 | else: 55 | ind = ball_query(self.radius, self.num_samples, points.permute(0, 2, 1).contiguous(), 56 | new_points.permute(0, 2, 1).contiguous(), False) 57 | grouped_points = group_gather_by_index(points, ind) 58 | grouped_points_new = grouped_points - new_points.unsqueeze(3) 59 | grouped_features = group_gather_by_index(features, ind) 60 | new_features = torch.cat([grouped_points_new, grouped_features], dim=1) 61 | return new_features 62 | 63 | class SetConv(nn.Module): 64 | def __init__(self, num_points, radius, num_samples, in_channels, out_channels): 65 | super(SetConv, self).__init__() 66 | 67 | self.sample = Sample(num_points) 68 | self.group = Group(radius, num_samples) 69 | 70 | layers = [] 71 | out_channels = [in_channels+3, *out_channels] 72 | for i in range(1, len(out_channels)): 73 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()] 74 | self.conv = nn.Sequential(*layers) 75 | 76 | def forward(self, points, features): 77 | new_points = self.sample(points) 78 | new_features = self.group(points, new_points, features) 79 | new_features = self.conv(new_features) 80 | new_features = new_features.max(dim=3)[0] 81 | return new_points, new_features 82 | 83 | class FlowEmbedding(nn.Module): 84 | def __init__(self, num_samples, in_channels, out_channels): 85 | super(FlowEmbedding, self).__init__() 86 | 87 | self.num_samples = num_samples 88 | 89 | self.group = Group(None, self.num_samples, knn=True) 90 | 91 | layers = [] 92 | out_channels = [2*in_channels+3, *out_channels] 93 | for i in range(1, len(out_channels)): 94 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()] 95 | self.conv = nn.Sequential(*layers) 96 | 97 | def forward(self, points1, points2, features1, features2): 98 | new_features = self.group(points2, points1, features2) 99 | new_features = torch.cat([new_features, features1.unsqueeze(3).expand(-1, -1, -1, self.num_samples)], dim=1) 100 | new_features = self.conv(new_features) 101 | new_features = new_features.max(dim=3)[0] 102 | return new_features 103 | 104 | class SetUpConv(nn.Module): 105 | def __init__(self, num_samples, in_channels1, in_channels2, out_channels1, out_channels2): 106 | super(SetUpConv, self).__init__() 107 | 108 | self.group = Group(None, num_samples, knn=True) 109 | 110 | layers = [] 111 | out_channels1 = [in_channels1+3, *out_channels1] 112 | for i in range(1, len(out_channels1)): 113 | layers += [nn.Conv2d(out_channels1[i - 1], out_channels1[i], 1, bias=True), nn.BatchNorm2d(out_channels1[i], eps=0.001), nn.ReLU()] 114 | self.conv1 = nn.Sequential(*layers) 115 | 116 | layers = [] 117 | if len(out_channels1) == 1: 118 | out_channels2 = [in_channels1+in_channels2+3, *out_channels2] 119 | else: 120 | out_channels2 = [out_channels1[-1]+in_channels2, *out_channels2] 121 | for i in range(1, len(out_channels2)): 122 | layers += [nn.Conv2d(out_channels2[i - 1], out_channels2[i], 1, bias=True), nn.BatchNorm2d(out_channels2[i], eps=0.001), nn.ReLU()] 123 | self.conv2 = nn.Sequential(*layers) 124 | 125 | def forward(self, points1, points2, features1, features2): 126 | new_features = self.group(points1, points2, features1) 127 | new_features = self.conv1(new_features) 128 | new_features = new_features.max(dim=3)[0] 129 | new_features = torch.cat([new_features, features2], dim=1) 130 | new_features = new_features.unsqueeze(3) 131 | new_features = self.conv2(new_features) 132 | new_features = new_features.squeeze(3) 133 | return new_features 134 | 135 | class FeaturePropagation(nn.Module): 136 | def __init__(self, in_channels1, in_channels2, out_channels): 137 | super(FeaturePropagation, self).__init__() 138 | 139 | layers = [] 140 | out_channels = [in_channels1+in_channels2, *out_channels] 141 | for i in range(1, len(out_channels)): 142 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()] 143 | self.conv = nn.Sequential(*layers) 144 | 145 | def forward(self, points1, points2, features1, features2): 146 | dist, ind = three_nn(points2.permute(0, 2, 1).contiguous(), points1.permute(0, 2, 1).contiguous()) 147 | dist = dist * dist 148 | dist[dist < 1e-10] = 1e-10 149 | inverse_dist = 1.0 / dist 150 | norm = torch.sum(inverse_dist, dim=2, keepdim=True) 151 | weights = inverse_dist / norm 152 | new_features = torch.sum(group_gather_by_index(features1, ind) * weights.unsqueeze(1), dim = 3) 153 | new_features = torch.cat([new_features, features2], dim=1) 154 | new_features = self.conv(new_features.unsqueeze(3)).squeeze(3) 155 | return new_features 156 | 157 | class PointsFusion(nn.Module): 158 | def __init__(self, in_channels, out_channels): 159 | super(PointsFusion, self).__init__() 160 | 161 | layers = [] 162 | out_channels = [in_channels, *out_channels] 163 | for i in range(1, len(out_channels)): 164 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()] 165 | 166 | self.conv = nn.Sequential(*layers) 167 | 168 | def knn_group(self, points1, points2, features2, k): 169 | ''' 170 | For each point in points1, query kNN points/features in points2/features2 171 | Input: 172 | points1: [B,3,N] 173 | points2: [B,3,N] 174 | features2: [B,C,N] 175 | Output: 176 | new_features: [B,4,N] 177 | nn: [B,3,N] 178 | grouped_features: [B,C,N] 179 | ''' 180 | points1 = points1.permute(0,2,1).contiguous() 181 | points2 = points2.permute(0,2,1).contiguous() 182 | _, nn_idx, nn = knn_points(points1, points2, K=k, return_nn=True) 183 | points_resi = nn - points1.unsqueeze(2).repeat(1,1,k,1) 184 | grouped_dist = torch.norm(points_resi, dim=-1, keepdim=True) 185 | grouped_features = knn_gather(features2.permute(0,2,1), nn_idx) 186 | new_features = torch.cat([points_resi, grouped_dist], dim=-1) 187 | 188 | return new_features.permute(0,3,1,2).contiguous(),\ 189 | nn.permute(0,3,1,2).contiguous(),\ 190 | grouped_features.permute(0,3,1,2).contiguous() 191 | 192 | def forward(self, points1, points2, features1, features2, k, t): 193 | ''' 194 | Input: 195 | points1: [B,3,N] 196 | points2: [B,3,N] 197 | features1: [B,C,N] (only for inference of additional features) 198 | features2: [B,C,N] (only for inference of additional features) 199 | k: int, number of kNN cluster 200 | t: [B], time step in (0,1) 201 | Output: 202 | fused_points: [B,3+C,N] 203 | ''' 204 | N = points1.shape[-1] 205 | B = points1.shape[0] # batch size 206 | 207 | new_features_list = [] 208 | new_grouped_points_list = [] 209 | new_grouped_features_list = [] 210 | 211 | for i in range(B): 212 | t1 = t[i] 213 | new_points1 = points1[i:i+1,:,:] 214 | new_points2 = points2[i:i+1,:,:] 215 | new_features1 = features1[i:i+1,:,:] 216 | new_features2 = features2[i:i+1,:,:] 217 | 218 | N2 = int(N*t1) 219 | N1 = N - N2 220 | 221 | k2 = int(k*t1) 222 | k1 = k - k2 223 | 224 | randidx1 = torch.randperm(N)[:N1] 225 | randidx2 = torch.randperm(N)[:N2] 226 | new_points = torch.cat((new_points1[:,:,randidx1], new_points2[:,:,randidx2]), dim=-1) 227 | 228 | new_features1, grouped_points1, grouped_features1 = self.knn_group(new_points, new_points1, new_features1, k1) 229 | new_features2, grouped_points2, grouped_features2 = self.knn_group(new_points, new_points2, new_features2, k2) 230 | 231 | new_features = torch.cat((new_features1, new_features2), dim=-1) 232 | new_grouped_points = torch.cat((grouped_points1, grouped_points2), dim=-1) 233 | new_grouped_features = torch.cat((grouped_features1, grouped_features2), dim=-1) 234 | 235 | new_features_list.append(new_features) 236 | new_grouped_points_list.append(new_grouped_points) 237 | new_grouped_features_list.append(new_grouped_features) 238 | 239 | new_features = torch.cat(new_features_list, dim=0) 240 | new_grouped_points = torch.cat(new_grouped_points_list, dim=0) 241 | new_grouped_features = torch.cat(new_grouped_features_list, dim=0) 242 | 243 | new_features = self.conv(new_features) 244 | new_features = torch.max(new_features, dim=1, keepdim=False)[0] 245 | weights = F.softmax(new_features, dim=-1) 246 | 247 | C = features1.shape[1] 248 | weights = weights.unsqueeze(1).repeat(1,3+C,1,1) 249 | fused_points = torch.cat([new_grouped_points, new_grouped_features], dim=1) 250 | fused_points = torch.sum(torch.mul(weights, fused_points), dim=-1, keepdim=False) 251 | 252 | return fused_points 253 | 254 | def knn_group_withI(points1, points2, intensity2, k): 255 | ''' 256 | Input: 257 | points1: [B,3,N] 258 | points2: [B,3,N] 259 | intensity2: [B,1,N] 260 | ''' 261 | points1 = points1.permute(0,2,1).contiguous() 262 | points2 = points2.permute(0,2,1).contiguous() 263 | _, nn_idx, nn = knn_points(points1, points2, K=k, return_nn=True) 264 | points_resi = nn - points1.unsqueeze(2).repeat(1,1,k,1) # [B,M,k,3] 265 | grouped_dist = torch.norm(points_resi, dim=-1, keepdim=True) 266 | grouped_features = knn_gather(intensity2.permute(0,2,1), nn_idx) # [B,M,k,1] 267 | new_features = torch.cat([points_resi, grouped_dist], dim=-1) 268 | 269 | # [B,5,M,k], [B,3,M,k], [B,1,M,k] 270 | return new_features.permute(0,3,1,2).contiguous(), \ 271 | nn.permute(0,3,1,2).contiguous(), \ 272 | grouped_features.permute(0,3,1,2).contiguous() -------------------------------------------------------------------------------- /models/models.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import datetime 4 | import numpy as np 5 | 6 | from .layers import SetConv, FlowEmbedding, SetUpConv, FeaturePropagation, PointsFusion 7 | 8 | class FlowNet3D(nn.Module): 9 | ''' 10 | Implementation of FlowNet3D (CVPR 2019) in PyTorch 11 | We refer to original Tensorflow implementation (https://github.com/xingyul/flownet3d) 12 | and open source PyTorch implementation (https://github.com/multimodallearning/flownet3d.pytorch) 13 | to implement the code for FlowNet3D. 14 | ''' 15 | def __init__(self): 16 | super(FlowNet3D, self).__init__() 17 | 18 | self.set_conv1 = SetConv(1024, 0.5, 16, 3, [32, 32, 64]) 19 | self.set_conv2 = SetConv(256, 1.0, 16, 64, [64, 64, 128]) 20 | self.flow_embedding = FlowEmbedding(64, 128, [128, 128, 128]) 21 | self.set_conv3 = SetConv(64, 2.0, 8, 128, [128, 128, 256]) 22 | self.set_conv4 = SetConv(16, 4.0, 8, 256, [256, 256, 512]) 23 | self.set_upconv1 = SetUpConv(8, 512, 256, [], [256, 256]) 24 | self.set_upconv2 = SetUpConv(8, 256, 256, [128, 128, 256], [256]) 25 | self.set_upconv3 = SetUpConv(8, 256, 64, [128, 128, 256], [256]) 26 | self.fp = FeaturePropagation(256, 3, [256, 256]) 27 | self.classifier = nn.Sequential( 28 | nn.Conv1d(256, 128, 1, bias=True), 29 | nn.BatchNorm1d(128, eps=0.001), 30 | nn.ReLU(), 31 | nn.Conv1d(128, 3, 1, bias=True) 32 | ) 33 | 34 | def forward(self, points1, points2, features1, features2): 35 | ''' 36 | Input: 37 | points1: [B,3,N] 38 | points2: [B,3,N] 39 | features1: [B,3,N] (colors for Flythings3D and zero for LiDAR) 40 | features2: [B,3,N] (colors for Flythings3D and zero for LiDAR) 41 | Output: 42 | flow: [B,3,N] 43 | ''' 44 | points1_1, features1_1 = self.set_conv1(points1, features1) 45 | points1_2, features1_2 = self.set_conv2(points1_1, features1_1) 46 | 47 | points2_1, features2_1 = self.set_conv1(points2, features2) 48 | points2_2, features2_2 = self.set_conv2(points2_1, features2_1) 49 | 50 | embedding = self.flow_embedding(points1_2, points2_2, features1_2, features2_2) 51 | 52 | points1_3, features1_3 = self.set_conv3(points1_2, embedding) 53 | points1_4, features1_4 = self.set_conv4(points1_3, features1_3) 54 | 55 | new_features1_3 = self.set_upconv1(points1_4, points1_3, features1_4, features1_3) 56 | new_features1_2 = self.set_upconv2(points1_3, points1_2, new_features1_3, torch.cat([features1_2, embedding], dim=1)) 57 | new_features1_1 = self.set_upconv3(points1_2, points1_1, new_features1_2, features1_1) 58 | new_features1 = self.fp(points1_1, points1, new_features1_1, features1) 59 | 60 | flow = self.classifier(new_features1) 61 | 62 | return flow 63 | 64 | class PointINet(nn.Module): 65 | def __init__(self, freeze=1): 66 | super(PointINet, self).__init__() 67 | self.flow = FlowNet3D() 68 | if freeze == 1: 69 | for p in self.parameters(): 70 | p.requires_grad = False 71 | 72 | self.fusion = PointsFusion(4, [64, 64, 128]) 73 | 74 | def forward(self, points1, points2, features1, features2, t): 75 | ''' 76 | Input: 77 | points1: [B,3+C,N] 78 | points2: [B,3+C,N] 79 | features1: [B,3,N] (zeros) 80 | features2: [B,3,N] 81 | ''' 82 | 83 | points1_feature = points1[:,3:,:].contiguous() 84 | points2_feature = points2[:,3:,:].contiguous() 85 | points1 = points1[:,:3,:].contiguous() 86 | points2 = points2[:,:3,:].contiguous() 87 | 88 | 89 | # Estimate 3D scene flow 90 | with torch.no_grad(): 91 | flow_forward = self.flow(points1, points2, features1, features2) 92 | flow_backward = self.flow(points2, points1, features2, features1) 93 | 94 | t = t.unsqueeze(1).unsqueeze(1) 95 | 96 | # Warp two input point clouds 97 | warped_points1_xyz = points1 + flow_forward * t 98 | warped_points2_xyz = points2 + flow_backward * (1-t) 99 | 100 | k = 32 101 | 102 | # Points fusion 103 | fused_points = self.fusion(warped_points1_xyz, warped_points2_xyz, points1_feature, points2_feature, k, t) 104 | 105 | return fused_points 106 | 107 | if __name__ == '__main__': 108 | net = PointINet(freeze=1).cuda() 109 | 110 | points1 = torch.rand(1,4,16384).cuda() 111 | points2 = torch.rand(1,4,16384).cuda() 112 | features1 = torch.rand(1,3,16384).cuda() 113 | features2 = torch.rand(1,3,16384).cuda() 114 | 115 | t = np.array([0.2]).astype('float32') 116 | t = torch.from_numpy(t).cuda() 117 | 118 | fused_points = net(points1, points2, features1, features2, t) 119 | 120 | print(fused_points.shape) -------------------------------------------------------------------------------- /models/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.autograd import Function 4 | import numpy as np 5 | import random 6 | import torch.optim as optim 7 | 8 | from pytorch3d.loss import chamfer_distance 9 | import emd_cuda 10 | 11 | def pdist2squared(x, y): 12 | xx = (x**2).sum(dim=1).unsqueeze(2) 13 | yy = (y**2).sum(dim=1).unsqueeze(1) 14 | dist = xx + yy - 2.0 * torch.bmm(x.permute(0, 2, 1), y) 15 | dist[dist != dist] = 0 16 | dist = torch.clamp(dist, 0.0, np.inf) 17 | return dist 18 | 19 | class ClippedStepLR(optim.lr_scheduler._LRScheduler): 20 | def __init__(self, optimizer, step_size, min_lr, gamma=0.1, last_epoch=-1): 21 | self.step_size = step_size 22 | self.min_lr = min_lr 23 | self.gamma = gamma 24 | super(ClippedStepLR, self).__init__(optimizer, last_epoch) 25 | 26 | def get_lr(self): 27 | return [max(base_lr * self.gamma ** (self.last_epoch // self.step_size), self.min_lr) 28 | for base_lr in self.base_lrs] 29 | 30 | def flow_criterion(pred_flow, flow, mask): 31 | loss = torch.mean(mask * torch.sum((pred_flow - flow) * (pred_flow - flow), dim=1) / 2.0) 32 | return loss 33 | 34 | def chamfer_loss(pc1, pc2): 35 | ''' 36 | Input: 37 | pc1: [B,3,N] 38 | pc2: [B,3,N] 39 | ''' 40 | pc1 = pc1.permute(0,2,1) 41 | pc2 = pc2.permute(0,2,1) 42 | chamfer_dist, _ = chamfer_distance(pc1, pc2) 43 | return chamfer_dist 44 | 45 | class EarthMoverDistanceFunction(torch.autograd.Function): 46 | @staticmethod 47 | def forward(ctx, xyz1, xyz2): 48 | xyz1 = xyz1.contiguous() 49 | xyz2 = xyz2.contiguous() 50 | assert xyz1.is_cuda and xyz2.is_cuda, "Only support cuda currently." 51 | match = emd_cuda.approxmatch_forward(xyz1, xyz2) 52 | cost = emd_cuda.matchcost_forward(xyz1, xyz2, match) 53 | ctx.save_for_backward(xyz1, xyz2, match) 54 | return cost 55 | 56 | @staticmethod 57 | def backward(ctx, grad_cost): 58 | xyz1, xyz2, match = ctx.saved_tensors 59 | grad_cost = grad_cost.contiguous() 60 | grad_xyz1, grad_xyz2 = emd_cuda.matchcost_backward(grad_cost, xyz1, xyz2, match) 61 | return grad_xyz1, grad_xyz2 62 | 63 | 64 | def earth_mover_distance(xyz1, xyz2, transpose=True): 65 | """Earth Mover Distance (Approx) 66 | 67 | Args: 68 | xyz1 (torch.Tensor): (b, 3, n1) 69 | xyz2 (torch.Tensor): (b, 3, n1) 70 | transpose (bool): whether to transpose inputs as it might be BCN format. 71 | Extensions only support BNC format. 72 | 73 | Returns: 74 | cost (torch.Tensor): (b) 75 | 76 | """ 77 | if xyz1.dim() == 2: 78 | xyz1 = xyz1.unsqueeze(0) 79 | if xyz2.dim() == 2: 80 | xyz2 = xyz2.unsqueeze(0) 81 | if transpose: 82 | xyz1 = xyz1.transpose(1, 2) 83 | xyz2 = xyz2.transpose(1, 2) 84 | cost = EarthMoverDistanceFunction.apply(xyz1, xyz2) 85 | return cost 86 | 87 | def EMD(pc1, pc2): 88 | ''' 89 | Input: 90 | pc1: [1,3,M] 91 | pc2: [1,3,M] 92 | Ret: 93 | d: torch.float32 94 | ''' 95 | pc1 = pc1.permute(0,2,1).contiguous() 96 | pc2 = pc2.permute(0,2,1).contiguous() 97 | d = earth_mover_distance(pc1, pc2, transpose=False) 98 | d = torch.mean(d)/pc1.shape[1] 99 | return d -------------------------------------------------------------------------------- /pretrain_model/flownet3d_kitti_odometry_maxbias1.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/pretrain_model/flownet3d_kitti_odometry_maxbias1.pth -------------------------------------------------------------------------------- /pretrain_model/interp_kitti.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/pretrain_model/interp_kitti.pth -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import numpy as np 4 | from torch.utils.data import DataLoader 5 | 6 | from models.models import PointINet 7 | from models.utils import chamfer_loss, EMD 8 | 9 | from data.interpolation_data import NuscenesDataset, KittiInterpolationDataset 10 | 11 | import argparse 12 | from tqdm import tqdm 13 | 14 | def parse_args(): 15 | parser = argparse.ArgumentParser(description='Test') 16 | parser.add_argument('--batch_size', type=int, default=1) 17 | parser.add_argument('--gpu', type=str, default='0') 18 | parser.add_argument('--root', type=str, default='') 19 | parser.add_argument('--npoints', type=int, default=16384) 20 | parser.add_argument('--pretrain_model', type=str, default='./pretrain_model/interp_kitti.pth') 21 | parser.add_argument('--pretrain_flow_model', type=str, default='./pretrain_model/flownet3d_kitti_odometry_maxbias5.pth') 22 | parser.add_argument('--dataset', type=str, default='kitti', help='kitti/nuscenes') 23 | parser.add_argument('--scenelist', type=str, default='') 24 | 25 | return parser.parse_args() 26 | 27 | def test(args): 28 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu 29 | if args.dataset == 'nuscenes': 30 | scene_list = args.scenelist 31 | test_set = NuscenesDataset(args.npoints, args.root, scene_list, True, 5, False) 32 | elif args.dataset == 'kitti': 33 | test_set = KittiInterpolationDataset(args.root, args.npoints, 5, False, True) 34 | 35 | test_loader = DataLoader(test_set, 36 | batch_size=args.batch_size, 37 | num_workers=4, 38 | pin_memory=True, 39 | drop_last=True) 40 | 41 | net = PointINet(freeze=1) 42 | net.load_state_dict(torch.load(args.pretrain_model)) 43 | net.flow.load_state_dict(torch.load(args.pretrain_flow_model)) 44 | net.eval() 45 | net.cuda() 46 | 47 | with torch.no_grad(): 48 | 49 | chamfer_loss_list = [] 50 | emd_loss_list = [] 51 | 52 | pbar = tqdm(test_loader) 53 | for data in pbar: 54 | 55 | ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t = data 56 | ini_pc = ini_pc.cuda(non_blocking=True) 57 | mid_pc = mid_pc.cuda(non_blocking=True) 58 | end_pc = end_pc.cuda(non_blocking=True) 59 | ini_color = ini_color.cuda(non_blocking=True) 60 | mid_color = mid_color.cuda(non_blocking=True) 61 | end_color = end_color.cuda(non_blocking=True) 62 | t = t.cuda().float() 63 | 64 | pred_mid_pc = net(ini_pc, end_pc, ini_color, end_color, t) 65 | 66 | cd = chamfer_loss(pred_mid_pc[:,:3,:], mid_pc[:,:3,:]) 67 | emd = EMD(pred_mid_pc[:,:3,:], mid_pc[:,:3,:]) 68 | 69 | cd = cd.squeeze().cpu().numpy() 70 | emd = emd.squeeze().cpu().numpy() 71 | 72 | chamfer_loss_list.append(cd) 73 | emd_loss_list.append(emd) 74 | 75 | pbar.set_description('CD:{:.3} EMD:{:.3}'.format(cd, emd)) 76 | 77 | chamfer_loss_array = np.array(chamfer_loss_list) 78 | emd_loss_array = np.array(emd_loss_list) 79 | mean_chamfer_loss = np.mean(chamfer_loss_array) 80 | mean_emd_loss = np.mean(emd_loss_array) 81 | 82 | print("Mean chamfer distance: ", mean_chamfer_loss) 83 | print("Mean earth mover's distance: ", mean_emd_loss) 84 | 85 | if __name__ == '__main__': 86 | args = parse_args() 87 | test(args) -------------------------------------------------------------------------------- /train_interp.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.utils.data import DataLoader 4 | import torch.optim as optim 5 | import os 6 | 7 | from data.interpolation_data import KittiInterpolationDataset 8 | from models.models import PointINet 9 | from models.utils import ClippedStepLR, chamfer_loss 10 | 11 | from tqdm import tqdm 12 | import argparse 13 | 14 | def parse_args(): 15 | parser = argparse.ArgumentParser(description='PointINet') 16 | 17 | parser.add_argument('--batch_size', type=int, default=2) 18 | parser.add_argument('--epochs', type=int, default=100) 19 | parser.add_argument('--init_lr', type=float, default=0.001) 20 | parser.add_argument('--min_lr', type=float, default=0.00001) 21 | parser.add_argument('--step_size_lr', type=int, default=10) 22 | parser.add_argument('--gamma_lr', type=float, default=0.7) 23 | parser.add_argument('--init_bn_momentum', type=float, default=0.5) 24 | parser.add_argument('--min_bn_momentum', type=float, default=0.01) 25 | parser.add_argument('--step_size_bn_momentum', type=int, default=10) 26 | parser.add_argument('--gamma_bn_momentum', type=float, default=0.5) 27 | parser.add_argument('--gpu', type=str, default='0') 28 | parser.add_argument('--root', type=str, default='') 29 | parser.add_argument('--save_dir', type=str, default='') 30 | parser.add_argument('--npoints', type=int, default=16384) 31 | parser.add_argument('--dataset', type=str, default='kitti', help='kitti/nuscenes') 32 | parser.add_argument('--pretrain_model', type=str, default='') 33 | parser.add_argument('--freeze', type=int, default=1) 34 | parser.add_argument('--use_wandb', type=int, default=1) 35 | 36 | return parser.parse_args() 37 | 38 | def init_weights(m): 39 | if isinstance(m, nn.Conv1d) or isinstance(m, nn.Conv2d): 40 | torch.nn.init.xavier_uniform_(m.weight) 41 | m.bias.data.fill_(0.0) 42 | elif isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d): 43 | m.weight.data.fill_(1.0) 44 | m.bias.data.fill_(0.0) 45 | 46 | def train(args): 47 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu 48 | train_dataset = KittiInterpolationDataset(args.root, args.npoints, 5, True, True) 49 | train_loader = DataLoader(train_dataset, 50 | batch_size=args.batch_size, 51 | num_workers=4, 52 | shuffle=True, 53 | pin_memory=True, 54 | drop_last=True) 55 | 56 | net = PointINet(args.freeze).cuda() 57 | if args.use_wandb: 58 | wandb.watch(net) 59 | 60 | net.flow.load_state_dict(torch.load(args.pretrain_model)) 61 | optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=args.init_lr) 62 | lr_scheduler = ClippedStepLR(optimizer, args.step_size_lr, args.min_lr, args.gamma_lr) 63 | def update_bn_momentum(epoch): 64 | for m in net.modules(): 65 | if isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d): 66 | m.momentum = max(args.init_bn_momentum * args.gamma_bn_momentum ** (epoch // args.step_size_bn_momentum), args.min_bn_momentum) 67 | 68 | best_train_loss = float('inf') 69 | 70 | for epoch in range(args.epochs): 71 | update_bn_momentum(epoch) 72 | net.train() 73 | count = 0 74 | total_loss = 0 75 | 76 | pbar = tqdm(enumerate(train_loader)) 77 | 78 | for i, data in pbar: 79 | ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t = data 80 | ini_pc = ini_pc.cuda(non_blocking=True) 81 | mid_pc = mid_pc.cuda(non_blocking=True) 82 | end_pc = end_pc.cuda(non_blocking=True) 83 | ini_color = ini_color.cuda(non_blocking=True) 84 | mid_color = mid_color.cuda(non_blocking=True) 85 | end_color = end_color.cuda(non_blocking=True) 86 | t = t.cuda().float() 87 | 88 | optimizer.zero_grad() 89 | pred_mid_pc = net(ini_pc, end_pc, ini_color, end_color, t) 90 | 91 | loss = chamfer_loss(pred_mid_pc[:,:3,:], mid_pc[:,:3,:]) 92 | 93 | loss.backward() 94 | optimizer.step() 95 | 96 | count += 1 97 | total_loss += loss.item() 98 | if i % 10 == 0: 99 | pbar.set_description('Train Epoch:{}[{}/{}({:.0f}%)]\tLoss: {:.6f}'.format( 100 | epoch+1, i, len(train_loader), 100. * i/len(train_loader), loss.item() 101 | )) 102 | 103 | lr_scheduler.step() 104 | total_loss = total_loss/count 105 | if args.use_wandb == 1: 106 | wandb.log({"loss":total_loss}) 107 | 108 | print('Epoch ', epoch+1, 'finished ', 'loss = ', total_loss) 109 | 110 | if total_loss < best_train_loss: 111 | torch.save(net.state_dict(), args.save_dir+'best_train.pth') 112 | best_train_loss = total_loss 113 | 114 | print('Best train loss: {:.4f}'.format(best_train_loss)) 115 | 116 | if __name__ == '__main__': 117 | args = parse_args() 118 | if args.use_wandb == 1: 119 | import wandb 120 | wandb.init(config=args, project='PointINet') 121 | 122 | train(args) -------------------------------------------------------------------------------- /train_sceneflow.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.utils.data import DataLoader 4 | import torch.optim as optim 5 | import os 6 | 7 | from data.sceneflow_data import Flythings3D, KittiSceneFlowDataset, KittiOdometrySceneflow, NuScenesFlow 8 | 9 | from models.models import FlowNet3D 10 | from models.utils import ClippedStepLR, flow_criterion, chamfer_loss 11 | 12 | from tqdm import tqdm 13 | import argparse 14 | 15 | def parse_args(): 16 | parser = argparse.ArgumentParser(description='FlowNet3d') 17 | 18 | parser.add_argument('--batch_size', type=int, default=2) 19 | parser.add_argument('--epochs', type=int, default=100) 20 | parser.add_argument('--init_lr', type=float, default=0.001) 21 | parser.add_argument('--min_lr', type=float, default=0.00001) 22 | parser.add_argument('--step_size_lr', type=int, default=10) 23 | parser.add_argument('--gamma_lr', type=float, default=0.7) 24 | parser.add_argument('--init_bn_momentum', type=float, default=0.5) 25 | parser.add_argument('--min_bn_momentum', type=float, default=0.01) 26 | parser.add_argument('--step_size_bn_momentum', type=int, default=10) 27 | parser.add_argument('--gamma_bn_momentum', type=float, default=0.5) 28 | parser.add_argument('--gpu', type=str, default='0') 29 | parser.add_argument('--root', type=str, default='') 30 | parser.add_argument('--save_dir', type=str, default='') 31 | parser.add_argument('--npoints', type=int, default=8192) 32 | parser.add_argument('--dataset', type=str, default='Flythings3D', help='Flythings3D/Kitti/nuscenes') 33 | parser.add_argument('--pretrain_model', type=str, default='') 34 | parser.add_argument('--max_bias', type=int, default=2) 35 | parser.add_argument('--freeze', type=int, default=1) 36 | parser.add_argument('--use_wandb', action='store_true') 37 | parser.add_argument('--train_type', type=str, default='init', help='init/refine') 38 | 39 | return parser.parse_args() 40 | 41 | def init_weights(m): 42 | if isinstance(m, nn.Conv1d) or isinstance(m, nn.Conv2d): 43 | torch.nn.init.xavier_uniform_(m.weight) 44 | m.bias.data.fill_(0.0) 45 | elif isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d): 46 | m.weight.data.fill_(1.0) 47 | m.bias.data.fill_(0.0) 48 | 49 | def train(args): 50 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu 51 | if args.dataset == 'Flythings3D': 52 | train_dataset = Flythings3D(npoints=args.npoints, root=args.root, train=True) 53 | elif args.dataset == 'Kitti': 54 | train_dataset = KittiSceneFlowDataset(args.root, args.npoints, True) 55 | else: 56 | raise('Invalid dataset') 57 | 58 | train_loader = DataLoader(train_dataset, 59 | batch_size=args.batch_size, 60 | num_workers=4, 61 | shuffle=True, 62 | pin_memory=True, 63 | drop_last=True) 64 | 65 | net = FlowNet3D().cuda() 66 | 67 | if args.use_wandb: 68 | wandb.watch(net) 69 | 70 | if args.dataset == 'Flythings3D': 71 | net.apply(init_weights) 72 | elif args.dataset == 'Kitti': 73 | net.load_state_dict(torch.load(args.pretrain_model)) 74 | else: 75 | raise('Invalid dataset') 76 | 77 | optimizer = optim.Adam(net.parameters(), lr=args.init_lr) 78 | lr_scheduler = ClippedStepLR(optimizer, args.step_size_lr, args.min_lr, args.gamma_lr) 79 | 80 | def update_bn_momentum(epoch): 81 | for m in net.modules(): 82 | if isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d): 83 | m.momentum = max(args.init_bn_momentum * args.gamma_bn_momentum ** (epoch // args.step_size_bn_momentum), args.min_bn_momentum) 84 | 85 | best_train_loss = float('inf') 86 | 87 | for epoch in range(args.epochs): 88 | update_bn_momentum(epoch) 89 | 90 | net.train() 91 | 92 | count = 0 93 | total_loss = 0 94 | pbar = tqdm(enumerate(train_loader)) 95 | 96 | for i, data in pbar: 97 | points1, points2, features1, features2, flow, mask1 = data 98 | points1 = points1.cuda(non_blocking=True) 99 | points2 = points2.cuda(non_blocking=True) 100 | features1 = features1.cuda(non_blocking=True) 101 | features2 = features2.cuda(non_blocking=True) 102 | flow = flow.cuda(non_blocking=True) 103 | mask1 = mask1.cuda(non_blocking=True).float() 104 | 105 | optimizer.zero_grad() 106 | pred_flow = net(points1, points2, features1, features2) 107 | 108 | loss = flow_criterion(pred_flow, flow, mask1) 109 | loss.backward() 110 | optimizer.step() 111 | 112 | count += 1 113 | total_loss += loss.item() 114 | 115 | if i % 10 == 0: 116 | pbar.set_description('Train Epoch:{}[{}/{}({:.0f}%)]\tLoss: {:.6f}'.format( 117 | epoch+1, i, len(train_loader), 100. * i/len(train_loader), loss.item() 118 | )) 119 | 120 | lr_scheduler.step() 121 | total_loss = total_loss/count 122 | if args.use_wandb: 123 | wandb.log({"loss":total_loss}) 124 | 125 | print('Epoch ', epoch+1, 'finished ', 'loss = ', total_loss) 126 | if total_loss < best_train_loss: 127 | torch.save(net.state_dict(), args.save_dir+'best_train.pth') 128 | best_train_loss = total_loss 129 | 130 | print('Best train loss: {:.4f}'.format(best_train_loss)) 131 | 132 | def train_unsupervised(args): 133 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu 134 | if args.dataset == 'Kitti': 135 | train_dataset = KittiOdometrySceneflow(root=args.root, npoints=args.npoints, max_bias=args.max_bias) 136 | elif args.dataset == 'nuscenes': 137 | scenes_list = './data/nuscenes_trainlist.txt' 138 | train_dataset = NuScenesFlow(root=args.root, npoints=args.npoints, scenes_list=scenes_list, max_bias=args.max_bias) 139 | 140 | train_loader = DataLoader(train_dataset, 141 | batch_size=args.batch_size, 142 | num_workers=4, 143 | shuffle=True, 144 | pin_memory=True, 145 | drop_last=True) 146 | 147 | net = FlowNet3D().cuda() 148 | 149 | if args.use_wandb: 150 | wandb.watch(net) 151 | 152 | net.load_state_dict(torch.load(args.pretrain_model)) 153 | 154 | optimizer = optim.Adam(net.parameters(), lr=args.init_lr) 155 | lr_scheduler = ClippedStepLR(optimizer, args.step_size_lr, args.min_lr, args.gamma_lr) 156 | 157 | def update_bn_momentum(epoch): 158 | for m in net.modules(): 159 | if isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d): 160 | m.momentum = max(args.init_bn_momentum * args.gamma_bn_momentum ** (epoch // args.step_size_bn_momentum), args.min_bn_momentum) 161 | 162 | best_train_loss = float('inf') 163 | 164 | for epoch in range(args.epochs): 165 | net.train() 166 | count = 0 167 | 168 | total_loss = 0 169 | 170 | pbar = tqdm(enumerate(train_loader)) 171 | 172 | for i, data in pbar: 173 | points1, points2, features1, features2 = data 174 | points1 = points1.cuda(non_blocking=True) 175 | points2 = points2.cuda(non_blocking=True) 176 | features1 = features1.cuda(non_blocking=True) 177 | features2 = features2.cuda(non_blocking=True) 178 | 179 | optimizer.zero_grad() 180 | pred_flow = net(points1, points2, features1, features2) 181 | trans_points1 = points1 + pred_flow 182 | 183 | loss = chamfer_loss(trans_points1, points2) 184 | 185 | loss.backward() 186 | optimizer.step() 187 | 188 | count += 1 189 | total_loss += loss.item() 190 | 191 | if i % 10 == 0: 192 | pbar.set_description('Train Epoch:{}[{}/{}({:.0f}%)]\tLoss: {:.6f}'.format( 193 | epoch+1, i, len(train_loader), 100. * i/len(train_loader), loss.item() 194 | )) 195 | 196 | lr_scheduler.step() 197 | total_loss = total_loss/count 198 | 199 | if args.use_wandb == 1: 200 | wandb.log({"loss":total_loss}) 201 | 202 | print('Epoch ', epoch+1, 'finished ', 'loss = ', total_loss) 203 | 204 | if total_loss < best_train_loss: 205 | torch.save(net.state_dict(), args.save_dir+'best_train.pth') 206 | best_train_loss = total_loss 207 | 208 | print('Best train loss: {:.4f}'.format(best_train_loss)) 209 | 210 | 211 | if __name__ == '__main__': 212 | args = parse_args() 213 | if args.use_wandb == 1: 214 | import wandb 215 | wandb.init(config=args, project='PointINet') 216 | 217 | if args.train_type == 'init': 218 | train(args) 219 | elif args.train_type == 'refine': 220 | train_unsupervised(args) --------------------------------------------------------------------------------