/SSC

Semantic Scene Completion

Primary LanguagePython

Semantic Scene Completion

This repo contains code for the following papers

Contents

  1. Installation
  2. Data Preparation
  3. Train and Test
  4. Visualization and Evaluation
  5. Citation

Installation

Environment

  • Ubuntu 16.04
  • python 3.6
  • CUDA 10.1

Requirements:

You can install the requirements by running pip install -r requirements.txt.

If you use other versions of PyTorch or CUDA, be sure to select the corresponding version of torch_scatter.

Data Preparation

Download dataset

The raw data can be found in SSCNet.

The repackaged data can be downloaded via Google Drive or BaiduYun(Access code:lpmk).

The repackaged data includes:

rgb_tensor   = npz_file['rgb']		# pytorch tensor of color image
depth_tensor = npz_file['depth']	# pytorch tensor of depth 
tsdf_hr      = npz_file['tsdf_hr']  	# flipped TSDF, (240, 144, 240)
tsdf_lr      = npz_file['tsdf_lr']  	# flipped TSDF, ( 60,  36,  60)
target_hr    = npz_file['target_hr']	# ground truth, (240, 144, 240)
target_lr    = npz_file['target_lr']	# ground truth, ( 60,  36,  60)
position     = npz_file['position']	# 2D-3D projection mapping index

Train and Test

Configure the data path in config.py

'train': '/path/to/your/training/data'

'val': '/path/to/your/testing/data'

Train

Edit the training script run_SSC_train.sh, then run

bash run_SSC_train.sh

Test

Edit the testing script run_SSC_test.sh, then run

bash run_SSC_test.sh

Visualization and Evaluation

comging soon

Citation

If you find this work useful in your research, please cite our paper(s):

@inproceedings{Li2020aicnet,
  author     = {Jie Li, Kai Han, Peng Wang, Yu Liu, and Xia Yuan},
  title      = {Anisotropic Convolutional Networks for 3D Semantic Scene Completion},
  booktitle  = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year       = {2020},
}

@InProceedings{Li2019ddr,
    author    = {Li, Jie and Liu, Yu and Gong, Dong and Shi, Qinfeng and Yuan, Xia and Zhao, Chunxia and Reid, Ian},
    title     = {RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion},
    booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    month     = {June},
    pages     = {7693--7702},
    year      = {2019}
}

@article{li2019palnet,
  title={Depth Based Semantic Scene Completion With Position Importance Aware Loss},
  author={Li, Jie and Liu, Yu and Yuan, Xia and Zhao, Chunxia and Siegwart, Roland and Reid, Ian and Cadena, Cesar},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={1},
  pages={219--226},
  year={2019},
  publisher={IEEE}

}