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 the field of binocular stereo matching, remarkable progress has been made by iterative
methods like RAFT-Stereo and CREStereo. However, most of these methods lose …

We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We
propose a novel GRU-based estimator that encodes pixel-wise probability distributions of …

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

In this paper, we propose a practical three-dimensional (3D) real-scene reconstruction
framework named Deep3D, which is paired with a deep learning based multi-view stereo …

Recent cost volume pyramid based deep neural networks have unlocked the potential of
efficiently leveraging high-resolution images for depth inference from multi-view stereo. In …

Deep learning has greatly promoted the development of multi-view stereo in recent years.
However, how to measure the reliability of the estimated depth map for practical applications …

We present PlanarRecon--a novel framework for globally coherent detection and
reconstruction of 3D planes from a posed monocular video. Unlike previous works that …

Abstract Multi-View Stereo (MVS) algorithms rely on common photometric consistency
measures and, therefore, in cases of low-textured surfaces tend to generate unreliable depth …

Multi-view Stereo (MVS) aims to reconstruct a 3D point cloud model from multiple views. In
recent years, learning-based MVS methods have received a lot of attention and achieved …