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 …

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 …

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 …

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 …

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 …

Stereo matching has become a key technique for 3D environment perception in intelligent
vehicles. For a considerable time, convolutional neural networks (CNNs) have remained the …

Gloss-free sign language translation (SLT) aims to develop well-performing SLT systems
with no requirement for the costly gloss annotations, but currently still lags behind gloss …

Geometric knowledge has been shown to be beneficial for the stereo matching task.
However prior attempts to integrate geometric insights into stereo matching algorithms have …