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![Architecture](./docs/architecture.png)
We provide code for reproducing results of the paper [SoftGroup for 3D Instance Segmentation on Point Clouds (CVPR 2022)](https://arxiv.org/abs/2203.01509)
We provide code for reproducing results of the paper **SoftGroup for 3D Instance Segmentation on Point Clouds (CVPR 2022)**
Author: Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, and Chang D. Yoo.
@ -25,93 +25,12 @@ Existing state-of-the-art 3D instance segmentation methods perform semantic segm
## 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 softgroup python=3.7
conda activate softgroup
```
2\) Clone the repository.
```
git clone https://github.com/thangvubk/SoftGroup.git --recursive
```
3\) Install the requirements.
```
cd SoftGroup
pip install -r requirements.txt
conda install -c bioconda google-sparsehash
```
4\) Install spconv
* Install the dependencies.
```
sudo apt-get install libboost-all-dev
sudo apt-get install libsparsehash-dev
```
* Compile the spconv library.
```
cd SoftGroup/lib/spconv
python setup.py bdist_wheel
pip install dist/{WHEEL_FILE_NAME}.whl
```
5\) Compile the external C++ and CUDA ops.
```
cd SoftGroup/lib/softgroup_ops
export CPLUS_INCLUDE_PATH={conda_env_path}/softgroup/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`.
Please refer to [installation guide](docs/installation.md).
## Data Preparation
1\) Download the [ScanNet](http://www.scan-net.org/) v2 dataset.
2\) Put the downloaded ``scans`` and ``scans_test`` folder as follows.
```
SoftGroup
├── dataset
│ ├── scannetv2
│ │ ├── scans
│ │ ├── scans_test
```
3\) Split and preprocess data
```
cd SoftGroup/dataset/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.
```
SoftGroup
├── dataset
│ ├── scannetv2
│ │ ├── scans
│ │ ├── scans_test
│ │ ├── train
│ │ ├── val
│ │ ├── test
│ │ ├── val_gt
```
Please refer to [data preparation](dataset/README.md) for preparing the S3DIS and ScanNet v2 dataset.
## Pretrained models