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



  • Python 3.7.0
  • Pytorch 1.1.0
  • CUDA 9.0

Virtual Environment

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


(1) Clone the 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.

  • 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.)

  • 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.

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.

├── 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_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 $PATH_TO_PRETRAIN_MODEL$


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