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

Over the years, learning-based multi-view stereo methods have achieved great success
based on their coarse-to-fine depth estimation frameworks. However, 3D CNN-based cost …

Given a set of calibrated images, Multiple View Stereo (MVS) applies end-to-end depth
inference network to recover scene structure. However, previous methods designed pixel …

Unmanned aerial vehicle (UAV) has the characteristics of strong maneuverability and wide
field of vision, and its application in real-time terrain perception and target capture is one of …

Recently, there has been a growing interest in 3D CNN-based stereo matching methods due
to their remarkable accuracy. However, the $ O (n^ 3) $ complexity of 3D convolution makes …

This paper addresses the growing demands for safety and comfort in intelligent robot
systems, particularly autonomous vehicles, where road conditions play a pivotal role in …

Multi-camera 3D semantic occupancy prediction is a critical task for autonomous driving,
playing a vital role in understanding the environment. Current methods mainly rely on …

Currently, learning-based multi-view stereo (MVS) has been dominated by the pipeline of
3D cost volume and regularization network over the static cost volume for depth regression …

Existing unsupervised Multi-View Stereo (MVS) methods generally construct supervision on
the basis of the photometric consistency loss, which suffers from unreliable supervision and …

Learning-based multi-view stereo (MVS) is gaining prominence as a method for 3D
reconstruction. However, existing methods in the process of feature learning fail to focus on …