metadata
license: bsd-3-clause
tags:
- 3d-instance-segmentation
- point-cloud
- scannet
- s3dis
- stpls3d
ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution
Pretrained weights for ISBNet (CVPR 2023).
- Paper: arXiv:2303.00246
- Code: github.com/VinAIResearch/ISBNet
Checkpoints
Full models (use with tools/test.py):
| Dataset | AP | AP_50 | Config | File |
|---|---|---|---|---|
| ScanNetV2 val | 56.8 | 73.3 | config | scannetv2/head_scannetv2_val.pth |
| ScanNetV2 val (lightweight) | 50.1 | 68.9 | config | scannetv2/head_small_scannetv2_val.pth |
| ScanNet200 val | 24.5 | 32.7 | config | scannet200/head_scannetv2_200_val.pth |
| S3DIS Area 5 | 56.3 | 67.5 | config | s3dis/head_s3dis_area5.pth |
| STPLS3D val | 51.2 | 66.7 | config | stpls3d/head_stpls3d_val.pth |
Pretrained 3D U-Net backbones (the pretrain: field of each config):
| Dataset | File |
|---|---|
| ScanNetV2 val | scannetv2/pretrain_scannetv2_val.pth |
| ScanNetV2 train+val (benchmark) | scannetv2/pretrain_scannetv2_trainval.pth |
| ScanNet200 val | scannet200/pretrain_scannetv2_200_val.pth |
| S3DIS Area 5 | s3dis/pretrain_s3dis_area5.pth |
| STPLS3D val | stpls3d/pretrain_stpls3d_val.pth |
Also included: scannetv2/head_3dyco_scannetv2_val.pth.
Usage
# Download everything into ./pretrains (matches the default `pretrain:` paths in the configs)
hf download TuanNgo/ISBNet --local-dir pretrains
# Evaluate
python3 tools/test.py configs/scannetv2/isbnet_scannetv2.yaml pretrains/scannetv2/head_scannetv2_val.pth
Citation
@inproceedings{ngo2023isbnet,
author={Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution},
year= {2023}
}