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https://github.com/V2AI/Det3D
World's first general purpose 3D object detection codebse.
https://github.com/V2AI/Det3D
3d-object-detection autonomous-driving deep-learning kitti nuscenes object-detection point-cloud pytorch
Last synced: 13 days ago
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World's first general purpose 3D object detection codebse.
- Host: GitHub
- URL: https://github.com/V2AI/Det3D
- Owner: V2AI
- License: apache-2.0
- Created: 2019-08-19T12:43:56.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-19T10:21:31.000Z (11 months ago)
- Last Synced: 2024-04-13T18:37:30.181Z (7 months ago)
- Topics: 3d-object-detection, autonomous-driving, deep-learning, kitti, nuscenes, object-detection, point-cloud, pytorch
- Language: Python
- Homepage: https://arxiv.org/abs/1908.09492
- Size: 3.5 MB
- Stars: 1,457
- Watchers: 39
- Forks: 300
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Det3D
A general 3D Object Detection codebase in PyTorch.
## 1. Introduction
Det3D is the first 3D Object Detection toolbox which provides off the box implementations of many 3D object detection algorithms such as PointPillars, SECOND, PIXOR, etc, as well as state-of-the-art methods on major benchmarks like KITTI(ViP) and nuScenes(CBGS). Key features of Det3D include the following aspects:
* Multi Datasets Support: KITTI, nuScenes, Lyft
* Point-based and Voxel-based model zoo
* State-of-the-art performance
* DDP & SyncBN
## 2. Installation
Please refer to [INSTALATION.md](INSTALLATION.md).
## 3. Quick Start
Please refer to [GETTING_STARTED.md](GETTING_STARTED.md).
## 4. Model Zoo
### 4.1 nuScenes
| | mAP | mATE | mASE | mAOE | mAVE | mAAE | NDS | ckpt |
| ----------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| [CBGS](https://github.com/poodarchu/Det3D/blob/master/examples/cbgs/configs/nusc_all_vfev3_spmiddleresnetfhd_rpn2_mghead_syncbn.py) | 49.9 | 0.335 | 0.256 | 0.323 | 0.251 | 0.197 | 61.3 | [link](https://drive.google.com/drive/folders/1rhamAqegE9iOp18tzQVam4rOMhHjjnRM?usp=sharing) |
| [PointPillar](examples/point_pillars/configs/nusc_all_point_pillars_mghead_syncbn.py) | 41.8 | 0.363 | 0.264 | 0.377 | 0.288 | 0.198 | 56.0 | [link](https://drive.google.com/drive/folders/1U0bkEQAhcxhDUD42nTCGC0uU0qaTO_Uv?usp=sharing) |
The original model and prediction files are available in the [CBGS README](https://github.com/poodarchu/Det3D/tree/master/examples/cbgs).
### 4.2 KITTI
### [Second](examples/second/configs/kitti_car_vfev3_spmiddlefhd_rpn1_mghead_syncbn.py) on KITTI(val) Dataset
```
car AP @0.70, 0.70, 0.70:
bbox AP:90.54, 89.35, 88.43
bev AP:89.89, 87.75, 86.81
3d AP:87.96, 78.28, 76.99
aos AP:90.34, 88.81, 87.66
```
### [PointPillars](examples/point_pillars/configs/kitti_point_pillars_mghead_syncbn.py) on KITTI(val) Dataset
```
car [email protected], 0.70, 0.70:
bbox AP:90.63, 88.86, 87.35
bev AP:89.75, 86.15, 83.00
3d AP:85.75, 75.68, 68.93
aos AP:90.48, 88.36, 86.58
```
### 4.3 Lyft
* [Lyft Config](https://github.com/poodarchu/Det3D/blob/master/examples/cbgs/configs/lyft_all_vfev3_spmiddleresnetfhd_rpn2_mghead_syncbn.py)
### 4.4 Waymo
## 5. Functionality
* Models
- [x] VoxelNet
- [x] SECOND
- [x] PointPillars
* Features
- [x] Multi task learning & Multi-task Learning
- [x] Distributed Training and Validation
- [x] SyncBN
- [x] Flexible anchor dimensions
- [x] TensorboardX
- [x] Checkpointer & Breakpoint continue
- [x] Self-contained visualization
- [x] Finetune
- [x] Multiscale Training & Validation
- [x] Rotated RoI Align
## 6. TODO List
* To Be Released
* [ ] [CGBS](examples/cbgs/configs/lyft_all_vfev3_spmiddleresnetfhd_rpn2_mghead_syncbn.py) on Lyft(val) Dataset
* Models
- [ ] PointRCNN
- [ ] PIXOR
## 7. Call for contribution.
* Support Waymo Dataset.
* Add other 3D detection / segmentation models, such as VoteNet, STD, etc.
## 8. Developers
[Benjin Zhu](https://github.com/poodarchu/) , [Bingqi Ma](https://github.com/a157801)
## 9. License
Det3D is released under the [Apache licenes](LICENES).
## 10. Citation
Det3D is a derivative codebase of [CBGS](https://arxiv.org/abs/1908.09492), if you find this work useful in your research, please consider cite:
```
@article{zhu2019class,
title={Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection},
author={Zhu, Benjin and Jiang, Zhengkai and Zhou, Xiangxin and Li, Zeming and Yu, Gang},
journal={arXiv preprint arXiv:1908.09492},
year={2019}
}
```
## 11. Acknowledgement
* [mmdetection](https://github.com/open-mmlab/mmdetection)
* [mmcv](https://github.com/open-mmlab/mmcv)
* [second.pytorch](https://github.com/traveller59/second.pytorch)
* [maskrcnn_benchmark](https://github.com/facebookresearch/maskrcnn-benchmark)