This repo is the official implementation of "Suppress-and-Refine Framework for End-to-End 3D Object Detection".
A simple, fast, efficient and end-to-end 3D object detector without NMS.
Method backbone mAP@0.25 mAP@0.5 Runtime (FPS) Ckpt VoteNet PointNet++ 62.9 39.9 10.8 - H3DNet 4xPointNet++ 67.2 48.1 4.4 - MLCVNet PointNet++ 64.5 41.4 6.7 - BRNet PointNet++ 66.1 50.9 8.7 - Group-Free PointNet++ 67.3 48.9 7.1 - Ours PointNet++ 66.2 53.5 13.5 model_ckpt Method backbone mAP@0.25 mAP@0.5 Ckpt VoteNet PointNet++ 59.1 35.8 - H3DNet 4xPointNet++ 60.1 39.0 - MLCVNet PointNet++ 59.8 - - BRNet PointNet++ 61.1 43.7 - Group-Free PointNet++ 63.0 45.2 - Ours PointNet++ 60.0 44.7 model_ckptThe FPS is tested on a V100 GPU.
InstallationThis repository is based on mmdetection3d, please follow this page for installation guidance.
Reproduce our results on SCANNET and SUNRGBDFor SCANNET.
CUDA_VISIBLE_DEVICES=0,1 PORT=29600 ./tools/dist_train.sh configs/sr/scannet_baseline.py 2
For SUNRGBD
CUDA_VISIBLE_DEVICES=0,1 PORT=29600 ./tools/dist_train.sh configs/sr/sunrgbd_baseline.py 2Evaluation
Please first download the ckpt from the ckpt link provided above.
Then for SCANNET.
./tools/dist_test.sh configs/sr/scannet_baseline.py epoch_30.pth 2 --eval mAP
For SUNRGBD
./tools/dist_test.sh configs/sr/sunrgbd_baseline.py epoch_33.pth 4 --eval mAP
Our code is based on wonderful mmdetection3d. Very apperciate their works!
If you find this project useful in your research, please consider cite:
@article{liu2021suppress,
title={Suppress-and-Refine Framework for End-to-End 3D Object Detection},
author={Liu, Zili and Xu, Guodong and Yang, Honghui and Chen, Minghao and Wu, Kuoliang and Yang, Zheng and Liu, Haifeng and Cai, Deng},
journal={arXiv preprint arXiv:2103.10042},
year={2021}
}
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