A general 3D Object Detection codebase in PyTorch
Det3D is the first 3D Object Detection toolbox which provides off the box implementations of many 3D object detection algorithms such as PointPillars, SECOND, PointRCNN, 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 apects:
- Multi Datasets Support: KITTI, nuScenes, Lyft, waymo
- Point-based and Voxel-based model zoo
- State-of-the-art performance
- DDP & SyncBN
KITTI(Val) | nuScenes(Val) | |
---|---|---|
VoxelNet | - | - |
SECOND | - | - |
PointPillars | - | - |
PIXOR | - | - |
PointRCNN | - | - |
CBGS | - | - |
ViP | - | - |
git clone https://github.com/poodarchu/Det3D --recursive
cd Det3D
pip install -r requirements.txt
python setup.py develop
@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}
}