We present an uncertainty learning framework for dense neural simultaneous localization
and mapping (SLAM). Estimating pixel-wise uncertainties for the depth input of dense SLAM …

Stereo is a prominent technique to infer dense depth maps from images, and deep learning
further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when …

Confidence measures aim at detecting unreliable depth measurements and play an
important role for many purposes and in particular, as recently shown, to improve stereo …

Confidence measures for stereo gained popularity in recent years due to their improved
capability to detect outliers and the increasing number of applications exploiting these cues …

Stereo matching is one of the most popular techniques to estimate dense depth maps by
finding the disparity between matching pixels on two, synchronized and rectified images …

Event stereo matching is an emerging technique to estimate depth from neuromorphic
cameras; however, events are unlikely to trigger in the absence of motion or the presence of …

Depth sensing is an important issue in many applications, such as Augmented Reality (AR),
eXtended Reality (XR), and Metaverse. For 3D reconstruction, a depth map can be acquired …

Confidence measures estimate unreliable disparity assignments performed by a stereo
matching algorithm and, as recently proved, can be used for several purposes. This paper …

Abstract The removal of Multi-Path Interference (MPI) is one of the major open challenges in
depth estimation with Time-of-Flight (ToF) cameras. In this paper we propose a novel …

This paper proposes a novel framework for the fusion of depth data produced by a Time-of-
Flight (ToF) camera and a stereo vision system. The key problem of balancing between the …