├── 3D Bounding box regressor.ipynb
├── CMakeLists.txt
├── Dataset creator.ipynb
├── Instance segmentation.ipynb
├── README.md
├── assets
├── 3d_car.jpeg
├── 3d_lidar.png
├── DBSCAN_0.png
├── car_1.jpeg
├── car_1.png
├── car_2.jpeg
├── frustum_2.png
├── instance.gif
└── original_pointcloud.png
├── dataset
├── 3d_obj_detection.csv
├── ann_points_cam
│ ├── img_pc_anno_63b89fe17f3e41ecbe28337e0e35db8e.txt
│ └── img_pc_anno_83d881a6b3d94ef3a3bc3b585cc514f8.txt
├── ann_points_lidar
│ ├── pc_anno_63b89fe17f3e41ecbe28337e0e35db8e.txt
│ └── pc_anno_83d881a6b3d94ef3a3bc3b585cc514f8.txt
├── detection_config.json
├── pcd_segmentation
│ ├── car1.pcd
│ ├── car2.pcd
│ ├── car3.pcd
│ └── car4.pcd
├── point_features
│ ├── car1cvfh_.txt
│ ├── car1esf_.txt
│ ├── car1ourcvfh_.txt
│ ├── car1vfh_.txt
│ ├── car2cvfh_.txt
│ ├── car2esf_.txt
│ ├── car2ourcvfh_.txt
│ ├── car2vfh_.txt
│ ├── car3cvfh_.txt
│ ├── car3esf_.txt
│ ├── car3ourcvfh_.txt
│ ├── car3vfh_.txt
│ ├── car4cvfh_.txt
│ ├── car4esf_.txt
│ ├── car4ourcvfh_.txt
│ └── car4vfh_.txt
└── samples
│ └── sample_ca9a282c9e77460f8360f564131a8af5_file.txt
├── requirements.txt
├── src
└── pcl_features.cpp
├── tutorials
└── 3d_to_2d_test_methods.ipynb
└── utils
├── __init__.py
├── eval.py
├── misc.py
└── vis.py
/CMakeLists.txt:
--------------------------------------------------------------------------------
1 | cmake_minimum_required (VERSION 3.5.1)
2 | project (FEATURE_EXTRACTION)
3 | find_package (PCL 1.8.1 REQUIRED)
4 |
5 | link_libraries(stdc++fs)
6 | include_directories(${PCL_INCLUDE_DIRS})
7 | link_directories(${PCL_LIBRARY_DIRS})
8 | add_definitions(${PCL_DEFINITIONS})
9 | add_executable(pcl_features src/pcl_features.cpp)
10 | target_link_libraries(pcl_features ${PCL_LIBRARIES})
11 |
12 |
--------------------------------------------------------------------------------
/README.md:
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2 | # 3D Object detection using classic Machine Learning
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4 | This repository aims to be an end-to-end pipeline for 3D object detection using classic Machine Learning (ML) technique. The idea is to prove that classic ML is capable to tackle complex tasks such as 3D object detection for self-driving vehicles' applications.
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