Depth Estimation from Single Image using Sparse Representations
This addresses depth estimation from single images, which is a challenging problem in computer vision, but the abstract suggests it may be incremental as it builds on existing sparse representation techniques.
The paper tackles monocular depth estimation by proposing a deep sparse coding method with deterministic dictionary initialization, but no concrete results or numbers are provided in the abstract.
Monocular depth estimation is an interesting and challenging problem as there is no analytic mapping known between an intensity image and its depth map. Recently there has been a lot of data accumulated through depth-sensing cameras, in parallel to that researchers started to tackle this task using various learning algorithms. In this paper, a deep sparse coding method is proposed for monocular depth estimation along with an approach for deterministic dictionary initialization.