chaomath/class-balanced-grouping-and-sampling-for-point-cloud-3d-object-detection

Winner's Code and Technical Report for the nuScenes 3D Object Detection challenge in WAD, CVPR 2019

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

This repository contains the Winner's Code and Technical Report of the nuScenes 3D Object Detection challenge in WAD, CVPR 2019.

Code will be made public before the CVPR 2020 submission deadline.

Technical Report

ArXiv

Results

Final Submission compared to PointPillars baseline.

	Modality	Map Data	External Data	mAP	mATE	mASE	mAOE	mAVE	mAAE	NDS
PointPillars	LiDAR	No	No	30.5	0.517	0.290	0.500	0.316	0.368	45.3
Ours	LiDAR	No	No	52.8	0.300	0.247	0.380	0.245	0.140	63.3

More details of the challenge can be found at nuScenes Detection Leaderboard.

Trained Models

• Model Checkpoint: LINK
• Detection Results Of Train/Val/Test Split.

The provided checkpoint is a single model with 51.91% mAP and 62.83% NDS. Its input grid size is (0.05m, 0.05m, 0.2m). The cell below shows result details on Val Split.

mAP: 0.5191
mATE: 0.3212
mASE: 0.2563
mAOE: 0.3092
mAVE: 0.2281
mAAE: 0.1980
NDS: 0.6283
Eval time: 128.4s
2019-08-20 15:26:32,578 Training INFO: Evaluation nusc: Nusc v1.0-trainval Evaluation
car Nusc dist [email protected], 1.0, 2.0, 4.0
72.88, 82.54, 85.90, 87.73 mean AP: 0.8227
truck Nusc dist [email protected], 1.0, 2.0, 4.0
30.76, 51.07, 59.57, 63.75 mean AP: 0.5129
construction_vehicle Nusc dist [email protected], 1.0, 2.0, 4.0
0.69, 6.33, 16.17, 22.54 mean AP: 0.11433
bus Nusc dist [email protected], 1.0, 2.0, 4.0
34.91, 58.73, 73.58, 77.44 mean AP: 0.61163
trailer Nusc dist [email protected], 1.0, 2.0, 4.0
1.33, 20.37, 40.35, 48.42 mean AP: 0.2762
barrier Nusc dist [email protected], 1.0, 2.0, 4.0
53.17, 64.34, 68.63, 70.98 mean AP: 0.6428
motorcycle Nusc dist [email protected], 1.0, 2.0, 4.0
45.94, 51.22, 52.41, 52.74 mean AP: 0.5058
bicycle Nusc dist [email protected], 1.0, 2.0, 4.0
25.26, 26.18, 26.25, 26.49 mean AP: 0.2605
pedestrian Nusc dist [email protected], 1.0, 2.0, 4.0
78.78, 80.25, 81.91, 83.65 mean AP: 0.8115
traffic_cone Nusc dist [email protected], 1.0, 2.0, 4.0
59.83, 61.38, 63.94, 67.85 mean AP: 0.6325

Citation

If you find this work useful in your research, please consider cite:

@ARTICLE{2019arXiv190809492Z,
       author = {{Zhu}, Benjin and {Jiang}, Zhengkai and {Zhou}, Xiangxin and
         {Li}, Zeming and {Yu}, Gang},
        title = "{Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computer Vision and Pattern Recognition},
         year = "2019",
        month = "Aug",
          eid = {arXiv:1908.09492},
        pages = {arXiv:1908.09492},
archivePrefix = {arXiv},
       eprint = {1908.09492},
 primaryClass = {cs.CV},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190809492Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}