By Jianbo Jiao, Yunchao Wei, Zequn Jie, Honghui Shi, Rynson W. H. Lau, Thomas S. Huang.
This repository contains the codes and models described in the paper "Geometry-Aware Distillation for Indoor Semantic Segmentation" . This work addresses the problem of semantic segmentation for indoor scenes, by incorporating geometry-aware knowledge implicitly.
- The project was implemented and tested with Python 2.7, PyTorch (version 0.3) and TorchVision (version 0.2.0) on Linux with GPUs. Please setup the environment according to the instructions first.
- Download the model with the script under folder "models".
- Run the main.py to evaluate the performance on the NYUD-v2 dataset for semantic segmentation.
- You may optionally store the network predictions (color-coded results) by uncommenting line 70 in main.py. The results will be saved to a folder "outimgs".
If you find these codes and models useful in your research, please cite:
@InProceedings{CVPR19_GAD,
author = {Jianbo Jiao, Yunchao Wei, Zequn Jie, Honghui Shi, Rynson Lau, Thomas S. Huang},
title = {Geometry-Aware Distillation for Indoor Semantic Segmentation},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year = {2019}
}
References
[1] Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus. Indoor Segmentation and Support Inference from RGBD Images. In Proceedings of the European Conference on Computer Vision 2012.
[2] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dolla ́r. Focal Loss for Dense Object Detection. In Proceedings of the IEEE International Conference on Computer Vision 2017.
[3] Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, Nassir Navab. Deeper Depth Prediction with Fully Convolutional Residual Networks. In Proceedings of the International Conference on 3D Vision 2016.
If you have any questions please email the authors