Uncertainty guided ranking loss for enhanced domain generalizable stereo-matching - Nallapati, Nikhil (Nanyang Technological University, 2024)
Stereo-matching is a fundamental technique for accurately estimating scene depth in numerous computer vision applications. However, prevailing deep learning-based stereo-matching networks frequently encounter challenges regarding generalization across different domains. Recent research endeavors have addressed this issue primarily by mitigating the detrimental impact of shortcut features stemming from excessive dependence on local chromatic attributes such as color, illumination, and texture.