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 …

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 …

In the stereo matching task, matching cost aggregation is crucial in both traditional methods
and deep neural network models in order to accurately estimate disparities. We propose two …

Stereo matching estimates the disparity between a rectified image pair, which is of great
importance to depth sensing, autonomous driving, and other related tasks. Previous works …

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 …

Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network
with coarse-grained (ie, point-, scribble-, and block-wise) supervisions, where only a small …

State-of-the-art stereo matching networks have difficulties in generalizing to new unseen
environments due to significant domain differences, such as color, illumination, contrast, and …

In this paper, we present a real-time salient object detection system based on the minimum
spanning tree. Due to the fact that background regions are typically connected to the image …

Despite the continuous advances in local stereo matching for years, most efforts are on
developing robust cost computation and aggregation methods. Little attention has been …

This paper presents a data-driven matching cost for stereo matching. A novel deep visual
correspondence embedding model is trained via Convolutional Neural Network on a large …