Multi-view stereo infers the 3D geometry from a set of images captured from several known
positions and viewpoints. It is one of the most important components of 3D reconstruction …

Neural surface reconstruction has been shown to be powerful for recovering dense 3D
surfaces via image-based neural rendering. However, current methods struggle to recover …

A point cloud as a collection of points is poised to bring about a revolution in acquiring and
generating three‐dimensional (3D) surface information of an object in 3D reconstruction …

Abstract Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in
matching tasks. However, all-pairs correlations lack non-local geometry knowledge and …

In this work, we present a unified framework for multi-modality 3D object detection, named
UVTR. The proposed method aims to unify multi-modality representations in the voxel space …

Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall
into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is …

We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct
neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that …

We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for
high-resolution multi-view stereo. With high computation speed and low memory …

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D
cost volumes to regularize and regress the output depth or disparity. These methods are …

Depth estimation is solved as a regression or classification problem in existing learning-
based multi-view stereo methods. Although these two representations have recently …