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 …

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and
Transparent surfaces held in conjunction with the New Trends in Image Restoration and …

We introduce a novel framework for training deep stereo networks effortlessly and without
any ground-truth. By leveraging state-of-the-art neural rendering solutions, we generate …

Surgical data science is devoted to enhancing the quality, safety, and efficacy of
interventional healthcare. While the use of powerful machine learning algorithms is …

Inferring the depth of transparent or mirror (ToM) surfaces represents a hard challenge for
either sensors, algorithms, or deep networks. We propose a simple pipeline for learning to …

Although existing stereo matching models have achieved continuous improvement, they
often face issues related to trustworthiness due to the absence of uncertainty estimation …

We introduce a novel approach for adapting deep stereo networks in a collaborative
manner. By building over principles of federated learning we develop a distributed …

This paper proposes a novel framework integrating the principles of active stereo in
standard passive camera systems without a physical pattern projector. We virtually project a …

Stereo matching is a core component in many computer vision and robotics systems.
Despite significant advances over the last decade, handling matching ambiguities in ill …

State-of-the-art stereo matching networks trained on in-domain data often underperform on
cross-domain scenes. Intuitively, leveraging the zero-shot capacity of a foundation model …