On robust cross-view consistency in self-supervised monocular depth estimation
Researchers propose new cross-view consistency losses to enhance self-supervision signal, achieving superior results in monocular depth estimation. The method leverages temporal coherence in depth feature space and 3D voxel space to mitigate the side effects of challenging cases.