Back to Search
Start Over
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching
- Publication Year :
- 2019
- Publisher :
- arXiv, 2019.
-
Abstract
- Disparity prediction from stereo images is essential to computer vision applications including autonomous driving, 3D model reconstruction, and object detection. To predict accurate disparity map, we propose a novel deep learning architecture for detectingthe disparity map from a rectified pair of stereo images, called MSDC-Net. Our MSDC-Net contains two modules: multi-scale fusion 2D convolution and multi-scale residual 3D convolution modules. The multi-scale fusion 2D convolution module exploits the potential multi-scale features, which extracts and fuses the different scale features by Dense-Net. The multi-scale residual 3D convolution module learns the different scale geometry context from the cost volume which aggregated by the multi-scale fusion 2D convolution module. Experimental results on Scene Flow and KITTI datasets demonstrate that our MSDC-Net significantly outperforms other approaches in the non-occluded region.<br />Comment: Accepted at ICIGP2019
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....7d3d118cd69dea452fe4e5cb40bd969e
- Full Text :
- https://doi.org/10.48550/arxiv.1904.12658