Pseudo Depth

Pseudo-depth, representing estimated depth information from single images or other limited data sources, is a rapidly developing area in computer vision aiming to overcome the limitations of relying solely on expensive or unavailable true depth sensors. Current research focuses on integrating pseudo-depth with various model architectures, including transformers and neural radiance fields (NeRFs), to improve tasks such as 3D hand pose estimation, motion segmentation, instance segmentation, and novel view synthesis. This work significantly impacts fields like robotics, augmented reality, and autonomous driving by enabling more robust and cost-effective solutions for tasks requiring depth perception, particularly in scenarios where traditional depth sensors are impractical. The accuracy and reliability of pseudo-depth methods are continuously being improved through techniques like multi-disparity consistency checks and depth-guided feature fusion.