Awesome
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)
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
And we provide the conda env config file here.
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)
ScanNet V2 | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
---|---|---|---|---|
cabinet | 0.4898 | 0.7634 | 0.2800 | 0.5349 |
bed | 0.8849 | 0.9506 | 0.7915 | 0.8642 |
chair | 0.9149 | 0.9357 | 0.8354 | 0.8604 |
sofa | 0.9049 | 0.9794 | 0.8027 | 0.9278 |
table | 0.6802 | 0.8486 | 0.6146 | 0.7600 |
door | 0.5955 | 0.7430 | 0.3721 | 0.5418 |
window | 0.4814 | 0.7092 | 0.2405 | 0.4078 |
bookshelf | 0.5876 | 0.8701 | 0.5032 | 0.7532 |
picture | 0.1716 | 0.3243 | 0.0687 | 0.1396 |
counter | 0.6085 | 0.8846 | 0.3545 | 0.5385 |
desk | 0.7538 | 0.9528 | 0.5481 | 0.7874 |
curtain | 0.6275 | 0.7910 | 0.4126 | 0.5224 |
refrigerator | 0.5467 | 0.9474 | 0.4882 | 0.8070 |
showercurtrain | 0.7349 | 0.9643 | 0.5189 | 0.6786 |
toilet | 0.9896 | 1.0000 | 0.9227 | 0.9310 |
sink | 0.5901 | 0.6735 | 0.3521 | 0.4490 |
bathtub | 0.8605 | 0.9355 | 0.8565 | 0.9032 |
garbagebin | 0.4726 | 0.7151 | 0.3169 | 0.5170 |
Overall | 0.6608 | 0.8327 | 0.5155 | 0.6624 |
SUN RGB-D | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
---|---|---|---|---|
bed | 0.8633 | 0.9553 | 0.6544 | 0.7592 |
table | 0.5136 | 0.8552 | 0.2981 | 0.5268 |
sofa | 0.6754 | 0.8931 | 0.5830 | 0.7193 |
chair | 0.7864 | 0.8723 | 0.6301 | 0.7137 |
toilet | 0.8699 | 0.9793 | 0.7125 | 0.8345 |
desk | 0.2929 | 0.8082 | 0.1134 | 0.4017 |
dresser | 0.3237 | 0.7615 | 0.2058 | 0.4954 |
night_stand | 0.5933 | 0.8627 | 0.4490 | 0.6588 |
bookshelf | 0.3394 | 0.7199 | 0.1574 | 0.3652 |
bathtub | 0.7505 | 0.8776 | 0.5383 | 0.6531 |
Overall | 0.6008 | 0.8585 | 0.4342 | 0.6128 |
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.