This tutorial presents a hands-on view of the field of multi-view stereo with a focus on
practical algorithms. Multi-view stereo algorithms are able to construct highly detailed 3D …

This paper presents a literature survey on existing disparity map algorithms. It focuses on
four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and …

We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

In this work, we present a new multi-view depth estimation method that utilizes both
conventional SfM reconstruction and learning-based priors over the recently proposed …

Despite the remarkable progress made by learning based stereo matching algorithms, one
key challenge remains unsolved. Current state-of-the-art stereo models are mostly based on …

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation
network for depth completion. The proposed network takes RGB and sparse depth images …

Deep learning has recently demonstrated its excellent performance for multi-view stereo
(MVS). However, one major limitation of current learned MVS approaches is the scalability …

We present a new, massively parallel method for high-quality multiview matching. Our work
builds on the Patchmatch idea: starting from randomly generated 3D planes in scene space …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

In this paper, we propose a novel explicit image filter called guided filter. Derived from a
local linear model, the guided filter computes the filtering output by considering the content …