Estimating depth from single RGB images and videos is of widespread interest due to its
applications in many areas, including autonomous driving, 3D reconstruction, digital …

We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

Monocular depth estimation is vital for scene understanding and downstream tasks. We
focus on the supervised setup, in which ground-truth depth is available only at training time …

Depth estimation from monocular images is pivotal for real-world visual perception systems.
While current learning-based depth estimation models train and test on meticulously curated …

Deep learning has substantially boosted the performance of Monocular Depth Estimation
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …

Traditionally, 3D indoor scene reconstruction from posed images happens in two phases:
per-image depth estimation, followed by depth merging and surface reconstruction …

Abstract Learning-based Multi-View Stereo (MVS) methods warp source images into the
reference camera frustum to form 3D volumes, which are fused as a cost volume to be …

Background and objective: Gastrointestinal (GI) endoscopy represents a promising tool for
GI cancer screening. However, the limited field of view and uneven skills of endoscopists …

Dense 3D reconstruction and ego-motion estimation are key challenges in autonomous
driving and robotics. Compared to the complex, multi-modal systems deployed today, multi …

Multi-frame depth estimation improves over single-frame approaches by also leveraging
geometric relationships between images via feature matching, in addition to learning …