Deep neural networks have achieved almost human-level results in various tasks and become popular the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by black-box point behavior of deep learning. The ensemble provides increased accuracy estimated uncertainty; however, linearly increasing size makes unfeasible for memory-intensive tasks. To address this ...

In monocular depth estimation, disturbances in the image context, like moving objects or reflecting materials, can easily lead to erroneous predictions. For that reason, uncertainty estimates for each pixel are necessary, particular safety-critical applications such as automated driving. We propose a post hoc estimation approach an already trained and thus fixed model, represented by deep neura...

This paper analyzes the problem of motion estimation from a sequence of stereo images. Two methods are considered and their differential and discrete approaches are compared. The differential approaches use differential optical flow whereas the discrete approaches use feature correspondences. Both methods are used to compute, first, the 3D velocity in the direction of the optical axis and, next...