Code for the paper SphereRPN: Learning Spheres for High-Quality Region Proposals on 3D Point Clouds Object Detection, ICIP 2021.

Authors: Thang Vu, Kookhoi Kim, Haeyong Kang, Xuan Thanh Nguyen, Tung M. Luu, Chang D. Yoo

This work was partly supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (2021-0-01381, Development of Causal AI through Video Understanding and Reinforcement Learning, and Its Applications to Real Environments), and partly supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2019-0-01396, Development of framework for analyzing, detecting, mitigating of bias in AI model and training data).

• Python 3.7.0
• Pytorch 1.1.0
• CUDA 9.0

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

(1) Clone the repository.

git clone https://github.com/thangvubk/SphereRPN.git --recursive 
cd SphereRPN

(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.

• To compile spconv, firstly install the dependent libraries.

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($INCLUDE_PATH$)

• Compile the spconv library.

cd lib/spconv
python setup.py bdist_wheel

• Run cd dist and use pip to install the generated .whl file.

(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.

(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.

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

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

You can start a tensorboard session by

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

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