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Implementation of our ECCV 2018 paper "Efficient Semantic Scene Completion Network with Spatial Group Convolution"

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SGC: Efficient Semantic Scene Completion Network with Spatial Group Convolution

Created by Jiahui Zhang, Hao Zhao, Anbang Yao, Yurong Chen, Li Zhang and Hongen Liao

Citation

If you find our work useful in your research, please consider citing:

    @inproceedings{zhang2018efficient,
      title={Efficient Semantic Scene Completion Network with Spatial Group Convolution},
      author={Zhang, Jiahui and Zhao, Hao and Yao, Anbang and Chen, Yurong and Zhang, Li and Liao, Hongen},
      booktitle={European Conference on Computer Vision (ECCV)},
      year={2018}
    }

Introduction

This work is based on our ECCV'18 paper. You can find the paper here for a quick overview. SGC is designed for accelerating the computation of 3D dense prediction tasks. We conduct experiments on the SUNCG dataset, achieving state-of-the-art performance (84.5% of IoU for scene completion and 70.5% IoU for semantic scene completion) and fast speed.

In this repository we release code on SUNCG dataset.

Installation

  1. Install SparseConvNet. This is a modified version of SparseConvNet. So you need to compile it yourself.

    Install the required packages:

    pip install torch==0.3.1
    pip install git+https://github.com/pytorch/tnt.git@master
    pip install msgpack
    pip install msgpack_numpy
    pip install cffi
    sudo apt-get install libsparsehash-dev
    pip install matplotlib
    

    Compile SparseConvNet:

    cd Pytorch
    python setup.py develop
  2. Install SUNCG data toolbox. We provide a python wrapper for the C++ functions about SUNCG Dataset.

    Compile SUNCG data toolbox. We have tested on boost 1.58.0 and python2

    cd ssc/suncg_data_tools
    mkdir build
    cd build
    cmake ..
    make

Generate data for training and testing

  1. Download SUNCG data, refer to SSCNet.

    cd ssc/
    mkdir data
    wget http://sscnet.cs.princeton.edu/sscnet_release/data/depthbin_eval.zip
    unzip depthbin_eval.zip
    wget http://sscnet.cs.princeton.edu/sscnet_release/data/SUNCGtrain.zip
    unzip SUNCGtrain.zip
    mv SUNCGtrain* depthbin/
  2. Prepare data used in our project. It will take a long time and generate about 700G data.

    cd ssc/suncg_data_tools/script
    python prepare_data.py
    python prepare_weight.py

Usage

Pretrained model are provided in ssc/baseline/log and ssc/sgc-pattern4/log.

  1. For baseline network without using SGC:

    cd ssc/baseline
    python sscnet
  2. For network with SGC:

    cd ssc/sgc-pattern4
    python sscnet

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Implementation of our ECCV 2018 paper "Efficient Semantic Scene Completion Network with Spatial Group Convolution"

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