Monocular Prior

Monocular priors leverage information from single images to improve 3D scene reconstruction, addressing the inherent ambiguity of using only visual data. Current research focuses on integrating these priors—often depth or normal maps—into neural implicit surface representations like signed distance functions (SDFs) and neural radiance fields (NeRFs), employing techniques like view-dependent normal compensation and uncertainty modeling to mitigate inconsistencies. This work aims to enhance the accuracy and efficiency of 3D reconstruction from limited viewpoints, impacting fields like augmented reality, robotics, and computer vision by enabling more robust and realistic 3D modeling from readily available monocular imagery.