VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking (CVPR 2023)

This is the official implementation of VoxelNeXt (CVPR 2023). VoxelNeXt is a clean, simple, and fully-sparse 3D object detector. The core idea is to predict objects directly upon sparse voxel features. No sparse-to-dense conversion, anchors, or center proxies are needed anymore. For more details, please refer to:

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking [Paper]
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia

News

[2023-04-03] VoxelNeXt is merged into the official OPenPCDet codebase.
[2023-01-28] VoxelNeXt achieved the SOTA performance on the Argoverse2 3D object detection.
[2022-11-11] VoxelNeXt achieved 1st on the nuScenes LiDAR tracking leaderboard.

Experimental results

nuScenes Detection	Set	mAP	NDS	Download
VoxelNeXt	val	60.5	66.6	Pre-trained
VoxelNeXt	test	64.5	70.0	Submission
+double-flip	test	66.2	71.4	Submission

nuScenes Tracking	Set	AMOTA	AMOTP	Download
VoxelNeXt	val	70.2	64.0	Results
VoxelNeXt	test	69.5	56.8	Submission
+double-flip	test	71.0	51.1	Submission

Argoverse2	Head Kernel	mAP	Download
VoxelNeXt	1x1x1	30.0	Pre-trained
VoxelNeXt	3x3x3	30.7	Pre-trained

Waymo	Vec_L1	Vec_L2	Ped_L1	Ped_L2	Cyc_L1	Cyc_L2
VoxelNeXt-2D	77.94/77.47	69.68/69.25	80.24/73.47	72.23/65.88	73.33/72.20	70.66/69.56
VoxelNeXt-K3	78.16/77.70	69.86/69.42	81.47/76.30	73.48/68.63	76.06/74.90	73.29/72.18

We cannot release the pre-trained models of VoxelNeXt on Waymo dataset due to the license of WOD.
For Waymo dataset, VoxelNeXt-K3 is an enhanced version of VoxelNeXt with larger model size.
During inference, VoxelNeXt can work either with sparse-max-pooling or NMS post-processing. Please install our implemented spconv-plus, if you want to use the sparse-max-pooling inference. Otherwise, please use NMS post-processing by default.

Getting Started

Installation

a. Clone this repository

https://github.com/dvlab-research/VoxelNeXt && cd VoxelNeXt

b. Install the environment

Following the install documents for OpenPCDet.

c. Prepare the datasets.

For nuScenes and Waymo datasets, please follow the document in OpenPCDet.

For Argoverse2 dataset, please follow the steps in the instruction.

Evaluation

We provide the trained weight file so you can just run with that. You can also use the model you trained.

cd tools 
bash scripts/dist_test.sh NUM_GPUS --cfg_file PATH_TO_CONFIG_FILE --ckpt PATH_TO_MODEL
#For example,
bash scripts/dist_test.sh 8 --cfg_file PATH_TO_CONFIG_FILE --ckpt PATH_TO_MODEL

Training

bash scripts/dist_train.sh NUM_GPUS --cfg_file PATH_TO_CONFIG_FILE
#For example,
bash scripts/dist_train.sh 8 --cfg_file PATH_TO_CONFIG_FILE

Citation

If you find this project useful in your research, please consider citing:

@inproceedings{chen2023voxenext,
  title={VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking},
  author={Yukang Chen and Jianhui Liu and Xiangyu Zhang and Xiaojuan Qi and Jiaya Jia},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2023}
}

Acknowledgement

This work is built upon the OpenPCDet and spconv.
This work is motivated by FSD. And we follow FSD for the Argoverse2 data processing.

Our Works in LiDAR-based Autonumous Driving

VoxelNeXt (CVPR 2023) [Paper] [Code] Fully Sparse VoxelNet for 3D Object Detection and Tracking.
Focal Sparse Conv (CVPR 2022 Oral) [Paper] [Code] Dynamic sparse convolution for high performance.
Spatial Pruned Conv (NeurIPS 2022) [Paper] [Code] 50% FLOPs saving for efficient 3D object detection.
LargeKernel3D (CVPR 2023) [Paper] [Code] Large-kernel 3D sparse CNN backbone.
SphereFormer (CVPR 2023) [Paper] [Code] Spherical window 3D transformer backbone.
spconv-plus A library where we combine our works into spconv.
SparseTransformer A library that includes high-efficiency transformer implementations for sparse point cloud or voxel data.

License

This project is released under the Apache 2.0 license.

alexandor91 / voxelnext Goto Github PK

voxelnext's Introduction