BRNet

Introduction

This is a release of the code of our paper Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds, CVPR 2021.

Authors: Bowen Cheng, Lu Sheng*, Shaoshuai Shi, Ming Yang, Dong Xu (*corresponding author)

[arxiv]

In this repository, we reimplement BRNet based on mmdetection3d for easier usage.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{cheng2021brnet,
  title={Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds},
  author={Cheng, Bowen and Sheng, Lu and Shi, Shaoshuai and Yang, Ming and Xu, Dong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

Installation

This repo is built based on mmdetection3d (V0.11.0), please follow the getting_started.md for installation.

The code is tested under the following environment:

• Ubuntu 16.04 LTS
• Python 3.7.10
• Pytorch 1.5.0
• CUDA 10.1
• GCC 7.3

Datasets

ScanNet

Please follow the instruction here to prepare ScanNet Data.

SUN RGB-D

Please follow the instruction here to prepare SUN RGB-D Data.

Download Trained Models

We provide the trained models of ScanNet and SUN RGB-D with per-class performances.

• ScanNet
  • GoogleDrive
  • BaiduNetdisk (Extraction Code: hv29)
• SUN RGB-D
  • GoogleDrive
  • BaiduNetdisk (Extraction Code: gnzu)

Note: Due to the detection results are unstable and fluctuate within 1~2 mAP points, the results here are slightly different from those in the paper.

Training

For ScanNet V2, please run:

CUDA_VISIBLE_DEVICES=0 python tools/train.py configs/brnet/brnet_8x1_scannet-3d-18class.py --seed 42

For SUN RGB-D, please run:

CUDA_VISIBLE_DEVICES=0 python tools/train.py configs/brnet/brnet_8x1_sunrgbd-3d-10class.py --seed 42

Demo

To test a 3D detector on point cloud data, please refer to Single modality demo and Point cloud demo in MMDetection3D docs.

Here, we provide a demo on SUN RGB-D dataset.

CUDA_VISIBLE_DEVICES=0 python demo/pcd_demo.py sunrgbd_000094.bin demo/brnet_8x1_sunrgbd-3d-10class.py checkpoints/brnet_8x1_sunrgbd-3d-10class_trained.pth

Visualization results

ScanNet

SUN RGB-D

Acknowledgments

Our code is heavily based on mmdetection3d. Thanks mmdetection3d Development Team for their awesome codebase.