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 method for extracting depth information from a rectified image pair. Our
approach focuses on the first stage of many stereo algorithms: the matching cost …

In this paper we present ActiveStereoNet, the first deep learning solution for active stereo
systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces …

Estimating the disparity and normal direction of one pixel simultaneously, instead of only
disparity, also known as 3D label methods, can achieve much higher subpixel accuracy in …

Deep-learning metrics have recently demonstrated extremely good performance to match
image patches for stereo reconstruction. However, training such metrics requires large …

Real-time stereo vision is attractive in many areas such as outdoor mapping and navigation.
As a popular accelerator in the image processing field, GPU is widely used for the studies of …

This contribution is intended to provide researchers with a comprehensive overview of the
current state-of-the-art concerning real-time 3D reconstruction methods suitable for medical …

Super-resolution (SR) techniques constitute a key element in image applications, which
need high-resolution reconstruction, while in the worst case, only a single low-resolution …

Dense disparity map extraction is one of the most active research areas in computer vision. It
tries to recover three-dimensional information from a stereo image pair. A large variety of …

Local matching is one of approaches for stereo matching which needs cost aggregation. In
Guided Filter based method proposed by Hosni, the cost map is smoothed by Guided Filter …