DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching
DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching
We present three multi-scale similarity learning architectures, or DeepSim networks. These models learn pixel-level matching with a contrastive loss and are agnostic to the geometry of the considered scene. We establish a middle ground between hybrid and end-to-end approaches by learning to densely allocate all corresponding pixels of an epipolar …