SoftGroup

We provide code for reproducing results of the paper SoftGroup for 3D Instance Segmentation on Point Clouds (CVPR 2022)

Author: Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, and Chang D. Yoo.

Introduction

Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results in (1) low overlaps between the predicted instance with the ground truth and (2) substantial false positives. To address the aforementioned problems, this paper proposes a 3D instance segmentation method referred to as SoftGroup by performing bottom-up soft grouping followed by top-down refinement. SoftGroup allows each point to be associated with multiple classes to mitigate the problems stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Experimental results on different datasets and multiple evaluation metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the strongest prior method by a significant margin of +6.2% on the ScanNet v2 hidden test set and +6.8% on S3DIS Area 5 of AP_50.

Feature

• State of the art performance on the ScanNet benchmark and S3DIS dataset (3/Mar/2022).
• High speed of 345 ms per scan on ScanNet dataset, which is comparable with the existing fastest methods (HAIS).
• Reproducibility code for both ScanNet and S3DIS datasets.

Installation

Please refer to installation guide.

Data Preparation

Please refer to data preparation for preparing the S3DIS and ScanNet v2 dataset.

Pretrained models

Dataset	AP	AP_50	AP_25	Download
S3DIS	51.4	66.5	75.4	model
ScanNet v2	46.0	67.6	78.9	model

Training

CUDA_VISIBLE_DEVICES=0 python train.py --config config/softgroup_default_scannet.yaml 

Inference

Testing for S3DIS dataset.

CUDA_VISIBLE_DEVICES=0 python test_s3dis.py --config config/softgroup_fold5_phase2_s3dis.yaml --pretrain $PATH_TO_PRETRAIN_MODEL$

Testing for ScanNet V2 dataset.

CUDA_VISIBLE_DEVICES=0 python test.py --config config/softgroup_default_scannet.yaml --pretrain $PATH_TO_PRETRAIN_MODEL$

Visualization

We provide visualization tools based on Open3D (tested on Open3D 0.8.0).

pip install open3D==0.8.0
python visualize_open3d.py --data_path {} --prediction_path {} --data_split {} --room_name {} --task {}

Please refer to visualize_open3d.py for more details.

Citation

If you find our work helpful for your research. Please cite our paper.

@inproceedings{vu2019softgroup,
  title={SoftGroup for 3D Instance Segmentation on 3D Point Clouds},
  author={Vu, Thang and Kim, Kookhoi and Luu, Tung M. and Nguyen, Xuan Thanh and Yoo, Chang D.},
  booktitle={CVPR},
  year={2022}
}