└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-Autonomous-Driving-Papers 2 | --- 3 | This repository provides awesome research papers for autonomous driving perception.
4 | I have tried my best to keep this repository up to date. If you do find a problem or have any suggestions, please raise this as 5 | an issue or make a pull request with information (format of the repo): Research paper title, datasets, metrics, objects, source code, publisher, and year. 6 | 7 | 8 | This summary is categorized into: 9 | - [Datasets](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#Datasets) 10 | - [LiDAR-based 3D Object Detection](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#1-lidar-based-3d-object-detection) 11 | - [Single-stage detectors](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#11-single-stage-detectors) 12 | - [Two-stage detectors](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#12-two-stage-detectors) 13 | - [Monocular Image-based 3D Object Detection](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#2-monocular-image-based-3d-object-detection) 14 | - [LiDAR and RGB Images fusion](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#3-lidar-and-rgb-images-fusion) 15 | - [Pseudo-LiDAR](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#4-pseudo-lidar) 16 | - [Training tricks](https://github.com/maudzung/Awesome-Autonomous-Driving-Papers#5-training-tricks) 17 | 18 | _**Abbreviations**_ 19 | - **AP-2D**: **A**verage **P**recision for 2D detection (on RGB-image space) 20 | - **AP-3D**: **A**verage **P**recision for 3D detection 21 | - **AP-BEV**: **A**verage **P**recision for Birds Eye View 22 | - **AOS**: **A**verage **O**rientation **S**imilarity _(if 2D bounding box available)_ 23 | 24 | ## Datasets 25 | - [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) 26 | - [Waymo Open Dataset](https://waymo.com/open/) 27 | - [nuScenes](https://www.nuscenes.org) 28 | - [Lift](https://self-driving.lyft.com/level5/data/) 29 | 30 | ## 1. LiDAR-based 3D Object Detection 31 | 32 | ### 1.1 Single-stage detectors 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 |

Research Paper	Datasets	Metrics	Objects	Source Code	Publisher	Year
“HorizonLiDAR3D”: 1st Place Solution for Waymo Open Dataset Challenge - 3D Detection and Domain Adaptation	Waymo	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	---	ArXiv	2020
Structure Aware Single-stage 3D Object Detection from Point Cloud (SA-SSD)	KITTI	AP-3D AP-BEV	Cars	PyTorch	CVPR	2020
3DSSD: Point-based 3D Single Stage Object Detector	KITTI nuScenes	AP-3D AP-BEV	Cars	PyTorch	CVPR	2020
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving	KITTI ATG4D	AP-3D AP-BEV	Cars	---	CVPR	2019
PointPillars: Fast Encoders for Object Detection from Point Clouds	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	CVPR	2019
SECOND: Sparsely Embedded Convolutional Detection	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	Sensors	2018
Complex-YOLO: an euler-region-proposal for real-time 3d object detection on point clouds	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	ECCV	2018
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud	KITTI	mAP-3D	Cars, Pedestrians, Cyclists	PyTorch	ECCV	2018
Pixor: Real-time 3d object detection from point clouds	KITTI	AP-3D	Cars	PyTorch	CVPR	2018
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch Tensorflow	CVPR	2018
3D Fully Convolutional Network using PointCloud data for Vehicle Detection	KITTI	AP-3D AOS	Cars	Tensorflow	IROS	2017

147 | 148 | 149 | ### 1.2 Two-stage detectors 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 |

Research Paper	Datasets	Metrics	Objects	Source Code	Publisher	Year
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection	KITTI Waymo	AP-3D	Cars	PyTorch	CVPR	2020
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud	KITTI	AP-3D AP-BEV	Cars (1 model), Pedestrians and Cyclists(1 model)	Tensorflow	CVPR	2020
3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds	KITTI	AP-3D AP-BEV	Cars	---	ArXiv	2020
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud	KITTI	AP-3D	Cars, Pedestrians, Cyclists	PyTorch	CVPR	2019
Patch Refinement - Localized 3D Object Detection	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	---	ArXiv	2019
StarNet: Targeted Computation for Object Detection in Point Clouds	KITTI Waymo	AP-3D	Cars, Pedestrians, Cyclists	PyTorch	ArXiv	2019
Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	ArXiv	2019
Fast Point R-CNN	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	---	ICCV	2019
STD: Sparse-to-Dense 3D Object Detector for Point Cloud	KITTI	AP-3D	Cars, Pedestrians, Cyclists	---	ICCV	2019
Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes	KITTI	AP-3D	Cars, Pedestrians, Cyclists	PyTorch	ICCV	2019
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection	KITTI SUN-RGBD	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	IROS	2019
Frustum PointNets for 3D Object Detection from RGB-D Data	KITTI SUN-RGBD	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	Tensorflow	CVPR	2018

273 | 274 | ## 2. Monocular Image-based 3D Object Detection 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 |

Research Paper	Datasets	Metrics	Objects	Source Code	Publisher	Year
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving	KITTI	AP-3D AP-BEV AOS	Cars	PyTorch	ECCV	2020
Stereo R-CNN based 3D Object Detection for Autonomous Driving	KITTI	AP-3D/BEV AP-2D	Cars	PyTorch	CVPR	2019
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection	KITTI	AP-3D/BEV AP-2D	Cars, Pedestrians, Cyclists	PyTorch	ICCV	2019
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors	KITTI	AP-3D/BEV AP-2D	Cars, Pedestrians, Cyclists	---	ArXiv	2019
3D Bounding Box Estimation Using Deep Learning and Geometry	KITTI	AP	Cars, Cyclists	PyTorch	CVPR	2017
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image	KITTI		Cars	---	CVPR	2017
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image	KITTI	AP-2D/3D AOS	Cars	Link	ICRA	2017

352 | 353 | ## 3. LiDAR and RGB Images Fusion 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 |

Research Paper	Datasets	Metrics	Objects	Source Code	Publisher	Year
ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes	SUN RGB-D	AP-3D	37 object categories	PyTorch	CVPR	2020
Multi-Task Multi-Sensor Fusion for 3D Object Detection	KITTI	AP-2D AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	CVPR	2019

387 | 388 | ## 4. Pseudo-LiDAR 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 |

Research Paper	Datasets	Metrics	Objects	Source Code	Publisher	Year
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	ICLR	2020
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	PyTorch	CVPR	2019

422 | 423 | ## 5. Training tricks 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | 450 | 451 | 452 | 453 | 454 | 455 | 456 | 457 | 458 | 459 | 460 | 461 | 462 | 463 | 464 |

Research Paper	Datasets	Metrics	Objects	Source Code	Publisher	Year
PPBA: Improving 3D Object Detection through Progressive Population Based Augmentation	KITTI	AP-3D AP-BEV	Cars, Pedestrians, Cyclists	---	ArXiv	2020
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection	KITTI	AP-3D AP-BEV	10 object categories	PyTorch	ArXiv	2019
Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance	ScanNet Semantic3D			---	ArXiv	2019

465 | 466 | ## 6. Object tracking (in progress) 467 | 468 | 469 | 470 | **To do list:** 471 | - [x] Add 3D object detection papers based on LiDAR/monocular images/fusion/pseudo-LiDAR. 472 | - [x] Add training tricks papers 473 | - [ ] Add object tracking papers. 474 | - [ ] Provide `download.py` script to automatically download `.pdf` files. 475 | 476 | 477 | ## References 478 | 479 | - The format of the README has been referred from [RedditSota/state-of-the-art-result-for-machine-learning-problems](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems) 480 | --------------------------------------------------------------------------------