This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other.
Training a single network for high resolution and geometrically consistent monocular depth estimation is challenging due to varying scene complexities in the real world.
... metric depth estimation. Relative depth estimation predicts the relative depth order among ... example, depth completion [38,39,55], which recovers a dense depth map from ... Depth Map Decomposition for Monocular Depth Estimation 19.
This is an ideal reference for anyone looking for an understanding of the diverse concepts and ideas around this topic and how we can move towards more general techniques than traditional photometric stereo.
... Depth map via monocular dense depth sensing, used to segment out objects in ... map generated using monoc- ular MVS is used to segment common items in ... example, we use several feature descriptor methods together for additional ...
During this project, state-of-the-art deep learning models have been used to estimate depth maps from a monocular RGB image applying a teacher-student learning approach.