We approach the problem by learning a similarity measure on small image patches using a convolutional neural network.
In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for various depth estimation tasks.
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data.
Ranked #7 on Monocular Depth Estimation on KITTI Eigen split (using extra training data)
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference.
Explicit representations of the global match distributions of pixel-wise correspondences between pairs of images are desirable for uncertainty estimation and downstream applications.