A lot of research has been conducted in recent years on stereo depth estimation techniques,
taking the traditional approach to a new level such that it is in an appreciably good form for …

Abstract Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for
accurately representing scenes. However, despite its superior novel view synthesis …

Stereo matching is close to hitting a half-century of history, yet witnessed a rapid evolution in
the last decade thanks to deep learning. While previous surveys in the late 2010s covered …

In this work, we present SuFIA, the first framework for natural language-guided augmented
dexterity for robotic surgical assistants. SuFIA incorporates the strong reasoning capabilities …

Omnidirectional stereo matching (OSM) is an essential and reliable means for depth
sensing. However, following earlier works on conventional stereo matching, prior state-of …

3D Gaussian Splatting (GS) significantly struggles to accurately represent the underlying 3D
scene geometry, resulting in inaccuracies and floating artifacts when rendering depth maps …

Stereo matching is a key technique for metric depth estimation in computer vision and
robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity …