jaedukseo / voxset

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)[paper]

Authors: Chenhang He, Ruihuang Li, Shuai Li, Lei Zhang.

This project is built on OpenPCDet.

Updates

2022-04-09: Add waymo config and multi-frame input.

The performance of VoxSeT (single-stage, single-frame) on Waymo valdation split are as follows.

	% Training	Car AP/APH	Ped AP/APH	Cyc AP/APH	Log file
Level 1	20%	72.10/71.59	77.94/69.58	69.88/68.54	Download
Level 2	20%	63.62/63.17	70.20/62.51	67.31/66.02
Level 1	100%	74.50/74.03	80.03/72.42	71.56/70.29	Download
Level 2	100%	65.99/65.56	72.45/65.39	68.95/67.73

Introduction

Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to compute the self-attention on large-scale point cloud data because point cloud is a long sequence and unevenly distributed in 3D space. To solve this issue, existing methods usually compute self-attention locally by grouping the points into clusters of the same size, or perform convolutional self-attention on a discretized representation. However, the former results in stochastic point dropout, while the latter typically has narrow attention fields. In this paper, we propose a novel voxel-based architecture, namely Voxel Set Transformer (VoxSeT), to detect 3D objects from point clouds by means of set-to-set translation. VoxSeT is built upon a voxel-based set attention (VSA) module, which reduces the self-attention in each voxel by two cross attentions and models features in a hidden space induced by a group of latent codes. With the VSA module, VoxSeT can manage voxelized point clusters with arbitrary size in a wide range, and process them in parallel with linear complexity. The proposed VoxSeT integrates the high performance of transformer with the efficiency of voxel-based model, which can be used as a good alternative to the convolutional and point-based backbones.

1. Recommended Environment

• Linux (tested on Ubuntu 16.04)
• Python 3.7
• PyTorch 1.9 or higher (tested on PyTorch 1.10.1)
• CUDA 9.0 or higher (tested on CUDA 10.2)

2. Set the Environment

pip install -r requirements.txt
python setup.py build_ext --inplace 

The torch_scatter package is required

3. Data Preparation

• Prepare KITTI dataset and road planes

# Download KITTI and organize it into the following form:
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2

# Generatedata infos:
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

4. Pretrain model

You can download the pretrain model here and the log file here.

The performance (using 11 recall poisitions) on KITTI validation set is as follows:

Car  [email protected], 0.70, 0.70:
bev  AP:90.1572, 88.0972, 86.8397
3d   AP:88.8694, 78.7660, 77.5758

Pedestrian [email protected], 0.50, 0.50:
bev  AP:63.1125, 58.5591, 55.1318
3d   AP:60.2515, 55.5535, 50.1888

Cyclist [email protected], 0.50, 0.50:
bev  AP:85.6768, 71.9008, 67.1551
3d   AP:85.4238, 70.2774, 64.9804

The runtime is about 33 ms per sample.

5. Train

• Train with a single GPU

python train.py --cfg_file tools/cfgs/kitti_models/voxset.yaml

• Train with multiple GPUs

cd VoxSeT/tools
bash scripts/dist_train.sh --cfg_file ./cfgs/kitti_models/voxset.yaml

6. Test with a pretrained model

cd VoxSeT/tools
python test.py --cfg_file --cfg_file ./cfgs/kitti_models/voxset.yaml --ckpt ${CKPT_FILE}

Citation

@inproceedings{he2022voxset,
  title={Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds},
  author={Chenhang He, Ruihuang Li, Shuai Li and Lei Zhang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2022}
}