Home

Awesome

SISNet

Semantic Scene Completion via Integrating Instances and Scene in-the-Loop (CVPR 2021)

In this repository, we provide SISNet model implementation (with Pytorch) as well as data preparation, training and evaluation scripts on NYU Depth V2, NYUCAD and SUNCG_RGBD.

image

Code is under construction.

Getting Started

Set up

Clone the repository:

git clone https://github.com/yjcaimeow/SISNet

Installation

The code is tested with Ubuntu 18.04, Pytorch v1.4.0, CUDA 10.0 and cuDNN v7.4.

conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch

Install the following Python dependencies (with pip install ):

matplotlib
opencv-python
plyfile
'trimesh>=2.35.39,<2.35.40'
'networkx>=2.2,<2.3'
tqdm
ninja
easydict
argparse
h5py
scipy

Compile the CUDA layers , which we used in the network:

cd pointnet2
python setup.py install --user

# Chamfer Distancecd
cd extensions/chamfer_dist
python setup.py install --user

# Cubic Feature Samplingcd
cd extensions/cubic_feature_sampling
python setup.py install --user

# Gridding & Gridding Reversecd
cd extensions/gridding
python setup.py install --user

# apex for multi-gpus train
cd ./furnace/apex
export CUDA_HOME=/usr/local/cuda-10.0
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Datasets

We use the nyu depth v2, nyucad and suncg_rgbd datasets in our experiments, which are available in ./data folder. The google drive link provides the input data we need (nyu depth v2 and nyucad).

Note that, for suncg_rgbd we follow and obtrain the data from SATNet. https://github.com/ShiceLiu/SATNet

For suncg_rgbd images, please download from the following link and unzip the SUNCGRGBD_images.zip under data/suncg/ https://pan.baidu.com/s/1vUt09GlkC1lPFRm8zXofZA

For the pre-process 2D semantic segmentation, we just use the following semantic segmentation code (https://github.com/Tramac/awesome-semantic-segmentation-pytorch) which includes many classical methods. So you can train your segmention to obtain the input data of initial scene completion or directly use the npz files provided in the Google Drive (voxel_bisenet_res34_4e-3).

Get Started

To train SISNet, you can simply use the following command:

bash scripts/train_SISNet.sh

To test SISNet, you can use the following command:

bash scripts/test_SISNet.sh

The results are save in results/${dataset}. You can use scripts/toply.py to transform the npz result to ply and use MeshLab to show.

Finally, the project folder is organized as follows.

SISNet
├── core
│ ├── scene_completion
│ ├── instance_completion
├── data
│ ├── nyucad
│ ├── suncg
│ ├── nyuv2
│ │ ├── train.txt
│ │ ├── test.txt
│ │ ├── R （RGB images）
│ │ ├── L (Label ground truth of semantic scene completion)
│ │ ├── M (Mapping from 2D to 3D)
│ │ ├── T (TSDF truncated signed distance function)
│ │ ├── voxel_bisenet_res34_4e-3（sem of visiable region）
├── instance_data
│ ├── nyuv2
├── nyuv2_S*_test/train_result
├── ckpts
│ ├── nyuv2_s*.pth
├── I*_ckpt
├── results
│ ├── nyuv2
│ │ ├── *_test/train_results
├── utils
│ ├── vis_ssc.py
│ ├── ...

Results on NYU Depth v2 Benchmark

image