SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks
Abstract
Dense pixel matching is important for many computer vision tasks such as disparity and flow estimation. We present a robust, unified descriptor network that considers a large context region with high spatial variance. Our network has a very large receptive field and avoids striding layers to maintain spatial resolution. These properties are achieved by creating a novel neural network layer that consists of multiple, parallel, stacked dilated convolutions (SDC). Several of these layers are combined to form our SDC descriptor network. In our experiments, we show that our SDC features outperform state-of-the-art feature descriptors in terms of accuracy and robustness. In addition, we demonstrate the superior performance of SDC in state-of-the-art stereo matching, optical flow and scene flow algorithms on several famous public benchmarks.
Cite
Text
Schuster et al. "SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00266Markdown
[Schuster et al. "SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/schuster2019cvpr-sdc/) doi:10.1109/CVPR.2019.00266BibTeX
@inproceedings{schuster2019cvpr-sdc,
title = {{SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks}},
author = {Schuster, Rene and Wasenmuller, Oliver and Unger, Christian and Stricker, Didier},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00266},
url = {https://mlanthology.org/cvpr/2019/schuster2019cvpr-sdc/}
}