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SPFormer

NEWS:🔥SPFormer is accepted at AAAI2023!🔥

Superpoint Transformer for 3D Scene Instance Segmentation

Jiahao Sun, Chunmei Qing, Junpeng Tan, Xiangmin Xu

PWC PWC

Introduction

​ Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D instance segmentation framework. 2) Existing method requires a time-consuming intermediate step of aggregation. To address these issues, this paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer. It groups potential features from point clouds into superpoints, and directly predicts instances through query vectors without relying on the results of object detection or semantic segmentation. The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances. Through bipartite matching based on superpoint masks, SPFormer can implement the network training without the intermediate aggregation step, which accelerates the network. Extensive experiments on ScanNetv2 and S3DIS benchmarks verify that our method is concise yet efficient. Notably, SPFormer exceeds compared state-of-the-art methods by 4.3% on ScanNetv2 hidden test set in terms of mAP and keeps fast inference speed (247ms per frame) simultaneously.

snapshot

The snapshot from ScanNetv2 benchmark testing server on 11/07/2022. SPFormer ranks top on the AP50 leadboard.

Installation

Requirements

  • Python 3.x
  • Pytorch 1.10
  • CUDA 10.x or higher

The following installation suppose python=3.8 pytorch=1.10 and cuda=11.4.

  • Create a conda virtual environment

    conda create -n spformer python=3.8
conda activate spformer

  • Clone the repository

    git clone https://github.com/sunjiahao1999/SPFormer.git

  • Install the dependencies

    Install Pytorch 1.10

    pip install spconv-cu114
conda install pytorch-scatter -c pyg
pip install -r requirements.txt

    Install segmentator from this repo (We wrap the segmentator in ScanNet).

  • Setup, Install spformer and pointgroup_ops.

    sudo apt-get install libsparsehash-dev
python setup.py develop
cd spformer/lib/
python setup.py develop

Data Preparation

ScanNet v2 dataset

Download the ScanNet v2 dataset.

Put the downloaded scans and scans_test folder as follows.

SPFormer
├── data
│   ├── scannetv2
│   │   ├── scans
│   │   ├── scans_test

Split and preprocess data

cd data/scannetv2
bash prepare_data.sh

The script data into train/val/test folder and preprocess the data. After running the script the scannet dataset structure should look like below.

SPFormer
├── data
│   ├── scannetv2
│   │   ├── scans
│   │   ├── scans_test
│   │   ├── train
│   │   ├── val
│   │   ├── test
│   │   ├── val_gt

Pretrained Model

Download SSTNet pretrained model (We only use the Sparse 3D U-Net backbone for training).

Move the pretrained model to checkpoints.

mkdir checkpoints
mv ${Download_PATH}/sstnet_pretrain.pth checkpoints/

Training

python tools/train.py configs/spf_scannet.yaml

Inference

Download SPFormer pretrain model and move it to checkpoints. Its performance on ScanNet v2 validation set is 56.3/73.9/82.9 in terms of mAP/mAP50/mAP25.

python tools/test.py configs/spf_scannet.yaml checkpoints/spf_scannet_512.pth

Visualization

Before visualization, you need to write the output results of inference.

python tools/test.py configs/spf_scannet.yaml ${CHECKPOINT} --out ${SAVE_PATH}

After inference, run visualization by execute the following command.

python tools/visualization.py --prediction_path ${SAVE_PATH}

You can visualize by Open3D or visualize saved .ply files on MeshLab. Arguments explaination can be found in tools/visualiztion.py.

Citation

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

@misc{2211.15766,
Author = {Jiahao Sun and Chunmei Qing and Junpeng Tan and Xiangmin Xu},
Title = {Superpoint Transformer for 3D Scene Instance Segmentation},
Year = {2022},
Eprint = {arXiv:2211.15766},
}

Ancknowledgement

Sincerely thanks for SoftGroup and SSTNet repos. This repo is build upon them.