shurans / sscnet

Semantic Scene Completion from a Single Depth Image

Semantic Scene Completion from a Single Depth Image

This repo contains training and testing code for our paper on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation. More information about the project can be found in our paper and project webset

If you find SSCNet useful in your research, please cite:

@article{song2016ssc,
  author     = {Song, Shuran and Yu, Fisher  and Zeng, Andy and Chang, Angel X and Savva, Manolis and Funkhouser, Thomas},
  title      = {Semantic Scene Completion from a Single Depth Image},
  journal    = {arXiv preprint arXiv:1611.08974},
  year       = {2016},
}

Contents

0. Organization
1. Installation
2. Quick Demo
3. Testing
4. Training
5. Visualization and Evaluation
6. Data Preparation

Organization

The code and data is organized as follows:

    sscnet
         |-- matlab_code
         |-- caffe_code
                    |-- caffe3d_suncg
                    |-- script
                         |-train
                         |-test   
         |-- data
                |-- depthbin
                    |-- NYUtrain 
                        |-- xxxxx_0000.png
                        |-- xxxxx_0000.bin
                    |-- NYUtest
                    |-- NYUCADtrain
                    |-- NYUCADtest
                    |-- SUNCGtest
                    |-- SUNCGtrain01
                    |-- SUNCGtrain02
                    |-- ...
                |-- eval
                    |-- NYUtest
                    |-- NYUCADtest
                    |-- SUNCGtest
            |-- models
            |-- results

Download

0. Download the data: download_data.sh (1.1 G) Updated on Sep 27 2017
1. Download the pretrained models: download_models.sh (9.9M)
2. [optional] Download the training data: download_suncgTrain.sh (16 G)
3. [optional] Download the results: download_results.sh (8.2G)

Installation

0. Software Requirements:

   1. Requirements for Caffe and pycaffe (see: Caffe installation instructions)
   2. Matlab 2016a or above with vision toolbox
   3. OPENCV

1. Hardware Requirements: at least 12G GPU memory.

2. Install caffe and pycaffe.

   1. Modify the config files based on your system. You can reference Makefile.config.sscnet_example.
   2. Compile

   cd caffe_code/caffe3d_suncg
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   make -j8 && make pycaffe

3. Export path

   export LD_LIBRARY_PATH=~/build_master_release/lib:/usr/local/cudnn/v5/lib64:~/anaconda2/lib:$LD_LIBRARY_PATH
   export PYTHONPATH=~/build_master_release/python:$PYTHONPATH

Quick Demo:

cd demo
python demotest_model.py

This demo runs semantic scene compeletion on one NYU depth map using our pretrained model and outputs a '.ply' visulization of the result.

Testing:

0. Run the testing script cd caffe_code/script/test python test_model.py
1. The output results will be stored in folder results in .hdf5 format
2. To test on other testsets (e.g. suncg, nyu, nyucad) you need to modify the paths in “test_model.py”.

Training:

0. Finetuning on NYU cd caffe_code/train/ftnyu ./train.sh
1. Training from scratch cd caffe_code/train/trainsuncg ./train.sh
2. To get more training data from SUNCG, please refer to the SUNCG toolbox

Visualization and Evaluation:

0. After testing, the results should be stored in folder results/

1. You can also download our precomputed results: ./download_results.sh

2. Run the evaluation code in matlab:

   matlab &
   cd matlab_code
   evaluation_script('../results/','nyucad')

3. The visualization of results will be stored in results/nyucad as “.ply” files.

Data

0. Data format
   1. Depth map : 16 bit png with bit shifting. Please refer to ./matlab_code/utils/readDepth.m for more information about the depth format.
   2. 3D volume: First three float stores the origin of the 3D volume in world coordinate. Then 16 float of camera pose in world coordinate. Followed by the 3D volume encoded by run-length encoding. Please refer to ./matlab_code/utils/readRLEfile.m for more details.
1. Example code to convert NYU ground truth data: matlab_code/perpareNYUCADdata.m This function provides an example of how to convert the NYU ground truth from 3D CAD model annotations provided by: Guo, Ruiqi, Chuhang Zou, and Derek Hoiem. "Predicting complete 3d models of indoor scenes." You need to download the original annotations by runing download_UIUCCAD.sh.
2. Example code to generate testing data without ground truth and room boundary: matlab_code/perpareDataTest.m This function provides an example of how to generate your own testing data without ground truth labels. It will generate a the .bin file with camera pose and an empty volume, without room boundary.

Generating training data from SUNCG

You can generate more training data from SUNCG by following steps:

0. Download SUNCG data and toolbox from: https://github.com/shurans/SUNCGtoolbox
1. Compile the toolbox.
2. Download the voxel data for objects (download_objectvox.sh) and move the folder under SUNCG data directory.
3. Run the script: genSUNCGdataScript() You may need to modify the following paths:suncgDataPath, SUNCGtoolboxPath, outputdir.

License

Code is released under the MIT License (refer to the LICENSE file for details).