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Stereo Matching

Stereo Matching is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.

Source: Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

Papers

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Showing 181–190 of 517 papers

TitleStatusHype
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular BackbonesCode2
Deep Laparoscopic Stereo Matching with TransformersCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
Robust and accurate depth estimation by fusing LiDAR and Stereo—0
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?—0
MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications—0
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation—0
WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite ImagesCode1
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