HAIS

Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021)

by Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang*. (*) Corresponding author. [arXiv]

Introduction

• HAIS is an efficient and concise bottom-up framework (NMS-free and single-forward) for point cloud instance segmentation. It adopts the hierarchical aggregation (point aggregation and set aggregation) to generate instances and the intra-instance prediction for outlier filtering and mask quality scoring.

• High performance. HAIS ranks 1st on the ScanNet benchmark (Aug. 8th, 2021).

• High speed. Thanks to the NMS-free and single-forward inference design, HAIS achieves the best inference speed among all existing methods. HAIS only takes 206 ms on RTX 3090 and 339 ms on TITAN X.

Method	Per-frame latency on TITAN X
ASIS	181913 ms
SGPN	158439 ms
3D-SIS	124490 ms
GSPN	12702 ms
3D-BoNet	9202 ms
GICN	8615 ms
OccuSeg	1904 ms
PointGroup	452 ms
HAIS	339 ms

[ICCV21 presentation]

Update

2021.9.30:

• Code is released.
• With better CUDA optimization, HAIS now only takes 339 ms on TITAN X, much better than the latency reported in the paper (410 ms on TITAN X).

Installation

1) Environment

• Python 3.x
• Pytorch 1.1 or higher
• CUDA 9.2 or higher
• gcc-5.4 or higher

Create a conda virtual environment and activate it.

conda create -n hais python=3.7
conda activate hais

2) Clone the repository.

git clone https://github.com/hustvl/HAIS.git --recursive

3) Install the requirements.

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

4) Install spconv

• Verify the version of spconv.

  spconv 1.0, compatible with CUDA < 11 and pytorch < 1.5, is already recursively cloned in HAIS/lib/spconv in step 2) by default.

  For higher version CUDA and pytorch, spconv 1.2 is suggested. Replace HAIS/lib/spconv with this fork of spconv.

git clone https://github.com/outsidercsy/spconv.git --recursive

  Note:  In the provided spconv 1.0 and 1.2, spconv\spconv\functional.py is modified to make grad_output contiguous. Make sure you use the modified spconv but not the original one. Or there would be some bugs of optimization.

• Install the dependent libraries.

conda install libboost
conda install -c daleydeng gcc-5 # (optional, install gcc-5.4 in conda env)

• Compile the spconv library.

cd HAIS/lib/spconv
python setup.py bdist_wheel

• Intall the generated .whl file.

cd HAIS/lib/spconv/dist
pip install {wheel_file_name}.whl

5) Compile the external C++ and CUDA ops.

cd HAIS/lib/hais_ops
export CPLUS_INCLUDE_PATH={conda_env_path}/hais/include:$CPLUS_INCLUDE_PATH
python setup.py build_ext develop

{conda_env_path} is the location of the created conda environment, e.g., /anaconda3/envs.

Data Preparation

1) Download the ScanNet v2 dataset.

2) Put the data in the corresponding folders.

• Copy the files [scene_id]_vh_clean_2.ply, [scene_id]_vh_clean_2.labels.ply, [scene_id]_vh_clean_2.0.010000.segs.json and [scene_id].aggregation.json into the dataset/scannetv2/train and dataset/scannetv2/val folders according to the ScanNet v2 train/val split.

• Copy the files [scene_id]_vh_clean_2.ply into the dataset/scannetv2/test folder according to the ScanNet v2 test split.

• Put the file scannetv2-labels.combined.tsv in the dataset/scannetv2 folder.

The dataset files are organized as follows.

HAIS
├── 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 HAIS/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/hais_run1_scannet.yaml 

Inference

1) To evaluate on validation set,

• prepare the .txt instance ground-truth files as the following.

cd dataset/scannetv2
python prepare_data_inst_gttxt.py

• set split and eval in the config file as val and True.

• Run the inference and evaluation code.

CUDA_VISIBLE_DEVICES=0 python test.py --config config/hais_run1_scannet.yaml --pretrain $PATH_TO_PRETRAIN_MODEL$

Pretrained model: Google Drive / Baidu Cloud [code: sh4t]. mAP/mAP50/mAP25 is 44.1/64.4/75.7.

2) To evaluate on test set,

• Set (split, eval, save_instance) as (test, False, True).
• Run the inference code. Prediction results are saved in HAIS/exp by default.

CUDA_VISIBLE_DEVICES=0 python test.py --config config/hais_run1_scannet.yaml --pretrain $PATH_TO_PRETRAIN_MODEL$

• Transform the prediction results into the submission format.
• Submit the results to the official evaluation server.

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.

Acknowledgement

The code is based on PointGroup and spconv.

Contact

If you have any questions or suggestions about this repo, please feel free to contact me (shaoyuchen@hust.edu.cn).

Citation

@InProceedings{Chen_2021_ICCV,
    author    = {Chen, Shaoyu and Fang, Jiemin and Zhang, Qian and Liu, Wenyu and Wang, Xinggang},
    title     = {Hierarchical Aggregation for 3D Instance Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {15467-15476}
}