SSTNet

Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks(ICCV2021) by Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia*. (*) Corresponding author. [arxiv]

Introduction

Instance segmentation in 3D scenes is fundamental in many applications of scene understanding. It is yet challenging due to the compound factors of data irregularity and uncertainty in the numbers of instances. State-of-the-art methods largely rely on a general pipeline that first learns point-wise features discriminative at semantic and instance levels, followed by a separate step of point grouping for proposing object instances. While promising, they have the shortcomings that (1) the second step is not supervised by the main objective of instance segmentation, and (2) their point-wise feature learning and grouping are less effective to deal with data irregularities, possibly resulting in fragmented segmentations. To address these issues, we propose in this work an end-to-end solution of Semantic Superpoint Tree Network (SSTNet) for proposing object instances from scene points. Key in SSTNet is an intermediate, semantic superpoint tree (SST), which is constructed based on the learned semantic features of superpoints, and which will be traversed and split at intermediate tree nodes for proposals of object instances. We also design in SSTNet a refinement module, termed CliqueNet, to prune superpoints that may be wrongly grouped into instance proposals.

Installation

Requirements

• Python 3.8.5
• Pytorch 1.7.1
• torchvision 0.8.2
• CUDA 11.1

then install the requirements:

pip install -r requirements.txt

SparseConv

For the SparseConv, please refer PointGroup's spconv to install.

Extension

This project is based on our Gorilla-Lab deep learning toolkit - gorilla-core and 3D toolkit gorilla-3d.

For gorilla-core, you can install it by running:

pip install gorilla-core==0.2.7.6

or building from source(recommend)

git clone https://github.com/Gorilla-Lab-SCUT/gorilla-core
cd gorilla-core
python setup.py install(develop)

For gorilla-3d, you should install it by building from source:

git clone https://github.com/Gorilla-Lab-SCUT/gorilla-3d
cd gorilla-3d
python setup.py develop

Tip: for high-version torch, the BuildExtension may fail by using ninja to build the compile system. If you meet this problem, you can change the BuildExtension in cmdclass={"build_ext": BuildExtension} as cmdclass={"build_ext": BuildExtension}.with_options(use_ninja=False)

Otherwise, this project also need other extension, we use the pointgroup_ops to realize voxelization and use the segmentator to generate superpoints for scannet scene. we use the htree to construct the Semantic Superpoint Tree and the hierarchical node-inheriting relations is realized based on the modified cluster.hierarchy.linkage function from scipy.

• For pointgroup_ops, we modified the package from PointGroup to let its function calls get rid of the dependence on absolute paths. You can install it by running:

  conda install -c bioconda google-sparsehash 
  cd $PROJECT_ROOT$
  cd sstnet/lib/pointgroup_ops
  python setup.py develop

  Then, you can call the function like:

  import pointgroup_ops
  pointgroup_ops.voxelization
  >>> <function Voxelization.apply>

• For htree, it can be seen as a supplement to the treelib python package, and I abstract the SST through both of them. You can install it by running:

  cd $PROJECT_ROOT$
  cd sstnet/lib/htree
  python setup.py install

  Tip: The interaction between this piece of code and treelib is a bit messy. I lack time to organize it, which may cause some difficulties for someone in understanding. I am sorry for this. At the same time, I also welcome people to improve it.

• For cluster, it is originally a sub-module in scipy, the SST construction requires the cluster.hierarchy.linkage to be implemented. However, the origin implementation do not consider the sizes of clustering nodes (each superpoint contains different number of points). To this end, we modify this function and let it support the property mentioned above. So, for used, you can install it by running:

  cd $PROJECT_ROOT$
  cd sstnet/lib/cluster
  python setup.py install

• For segmentator, please refer here to install. (We wrap the segmentator in ScanNet)

Data Preparation

Please refer to the README.md in data/scannetv2 to realize data preparation.

Training

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

You can start a tensorboard session by

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

Tip: For the directory of logging, please refer the implementation of function gorilla.collect_logger.

Inference and Evaluation

CUDA_VISIBLE_DEVICES=0 python test.py --config config/default.yaml --pretrain pretrain.pth --eval

• --split is the evaluation split of dataset.
• --save is the action to save instance segmentation results.
• --eval is the action to evaluate the segmentation results.
• --semantic is the action to evaluate semantic segmentation only (work on the --eval mode).
• --log-file is to define the logging file to save evaluation result (default please to refer the gorilla.collect_logger).
• --visual is the action to save visualization of instance segmentation. (It will be mentioned in the next partion.)

Results on ScanNet Benchmark

Rank 1st on the ScanNet benchmark

Pretrained

We provide a pretrained model trained on ScanNet(v2) dataset. [Google Drive] [Baidu Cloud] (提取码：f3az) Its performance on ScanNet(v2) validation set is 49.4/64.9/74.4 in terms of mAP/mAP50/mAP25.

Acknowledgement

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

Contact

If you have any questions or suggestions about this repo or paper, please feel free to contact me in issue or email ([email protected]).

TODO

• Distributed training(not verification)
• Batch inference
• Multi-processing for getting superpoints

Citation

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

@misc{liang2021instance,
      title={Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks}, 
      author={Zhihao Liang and Zhihao Li and Songcen Xu and Mingkui Tan and Kui Jia},
      year={2021},
      eprint={2108.07478},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}