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 …

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

In this paper, we present a novel recurrent multi-view stereo network based on long short-
term memory (LSTM) with adaptive aggregation, namely AA-RMVSNet. We firstly introduce …

Generating realistic 3D worlds occupied by moving humans has many applications in
games, architecture, and synthetic data creation. But generating such scenes is expensive …

Abstract Learning-based Multi-View Stereo (MVS) methods warp source images into the
reference camera frustum to form 3D volumes, which are fused as a cost volume to be …

In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo
net with dynamic consistency checking, namely D^ 2 HC-RMVSNet, for accurate dense point …

Abstract 3D reconstruction of scenes using multiple images, relying on robust
correspondence search and depth estimation, has been thoroughly studied for the two‐view …

Abstract Recent cascade Multi-View Stereo (MVS) methods can efficiently estimate high-
resolution depth maps through narrowing hypothesis ranges. However, previous methods …

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost
volumes. Due to the high computation and memory consumption of 3D CNN, the resolution …

Self-supervised methods play an increasingly important role in monocular depth estimation
due to their great potential and low annotation cost. To close the gap with supervised …