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PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (CVPR2020)

overview

Code for the paper PointGroup:Dual-Set Point Grouping for 3D Instance Segmentation, CVPR 2020 (Oral).

Authors: Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia

[arxiv] [video]

Introduction

Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. In this paper, we present PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. We design a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. Further, we formulate the ScoreNet to evaluate the candidate instances, followed by the Non-Maximum Suppression (NMS) to remove duplicates.

Installation

Requirements

Virtual Environment

conda create -n pointgroup python==3.7
source activate pointgroup

Install PointGroup

(1) Clone the PointGroup repository.

git clone https://github.com/llijiang/PointGroup.git --recursive 
cd PointGroup

(2) Install the dependent libraries.

pip install -r requirements.txt
conda install -c bioconda google-sparsehash

(3) For the SparseConv, we apply the implementation of spconv. The repository is recursively downloaded at step (1). We use the version 1.0 of spconv.

Note: We further modify spconv\spconv\functional.py to make grad_output contiguous. Make sure you use our modified spconv.

conda install libboost
conda install -c daleydeng gcc-5 # need gcc-5.4 for sparseconv

Add the $INCLUDE_PATH$ that contains boost in lib/spconv/CMakeLists.txt. (Not necessary if it could be found.)

include_directories($INCLUDE_PATH$)

cd lib/spconv
python setup.py bdist_wheel

(4) Compile the pointgroup_ops library.

cd lib/pointgroup_ops
python setup.py develop

If any header files could not be found, run the following commands.

python setup.py build_ext --include-dirs=$INCLUDE_PATH$
python setup.py develop

$INCLUDE_PATH$ is the path to the folder containing the header files that could not be found.

Data Preparation

(1) Download the ScanNet v2 dataset.

(2) Put the data in the corresponding folders.

The dataset files are organized as follows.

PointGroup
├── dataset
│   ├── scannetv2
│   │   ├── train
│   │   │   ├── [scene_id]_vh_clean_2.ply & [scene_id]_vh_clean_2.labels.ply & [scene_id]_vh_clean_2.0.010000.segs.json & [scene_id].aggregation.json
│   │   ├── val
│   │   │   ├── [scene_id]_vh_clean_2.ply & [scene_id]_vh_clean_2.labels.ply & [scene_id]_vh_clean_2.0.010000.segs.json & [scene_id].aggregation.json
│   │   ├── test
│   │   │   ├── [scene_id]_vh_clean_2.ply 
│   │   ├── scannetv2-labels.combined.tsv

(3) Generate input files [scene_id]_inst_nostuff.pth for instance segmentation.

cd dataset/scannetv2
python prepare_data_inst.py --data_split train
python prepare_data_inst.py --data_split val
python prepare_data_inst.py --data_split test

Training

CUDA_VISIBLE_DEVICES=0 python train.py --config config/pointgroup_run1_scannet.yaml

You can start a tensorboard session by

tensorboard --logdir=./exp --port=6666

Inference and Evaluation

(1) If you want to evaluate on validation set, prepare the .txt instance ground-truth files as the following.

cd dataset/scannetv2
python prepare_data_inst_gttxt.py

Make sure that you have prepared the [scene_id]_inst_nostuff.pth files before.

(2) Test and evaluate.

a. To evaluate on validation set, set split and eval in the config file as val and True. Then run

CUDA_VISIBLE_DEVICES=0 python test.py --config config/pointgroup_run1_scannet.yaml

An alternative evaluation method is to set save_instance as True, and evaluate with the ScanNet official evaluation script.

b. To run on test set, set (split, eval, save_instance) as (test, False, True). Then run

CUDA_VISIBLE_DEVICES=0 python test.py --config config/pointgroup_run1_scannet.yaml

c. To test with a pretrained model, run

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

Pretrained Model

We provide a pretrained model trained on ScanNet v2 dataset. Download it here. Its performance on ScanNet v2 validation set is 35.2/57.1/71.4 in terms of mAP/mAP50/mAP25.

Visualize

To visualize the point cloud, you should first install mayavi. Then you could visualize by running

cd util 
python visualize.py --data_root $DATA_ROOT$ --result_root $RESULT_ROOT$ --room_name $ROOM_NAME$ --room_split $ROOM_SPLIT$ --task $TASK$

The visualization task could be input, instance_gt, instance_pred, semantic_pred and semantic_gt.

Results on ScanNet Benchmark

Quantitative results on ScanNet test set at the submisison time. scannet_result

TODO List

Citation

If you find this work useful in your research, please cite:

@article{jiang2020pointgroup,
  title={PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation},
  author={Jiang, Li and Zhao, Hengshuang and Shi, Shaoshuai and Liu, Shu and Fu, Chi-Wing and Jia, Jiaya},
  journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}

Acknowledgement

This repo is built upon several repos, e.g., SparseConvNet, spconv and ScanNet.

Contact

If you have any questions or suggestions about this repo, please feel free to contact me (lijiang@cse.cuhk.edu.hk).