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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

Showing 125 of 517 papers

TitleStatusHype
FoundationStereo: Zero-Shot Stereo MatchingCode7
A Survey on Deep Stereo Matching in the TwentiesCode4
MonSter: Marry Monodepth to Stereo Unleashes PowerCode4
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo MatchingCode4
GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo ViewsCode3
An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022Code3
Unifying Flow, Stereo and Depth EstimationCode3
RoadBEV: Road Surface Reconstruction in Bird's Eye ViewCode3
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono FailCode3
DEFOM-Stereo: Depth Foundation Model Based Stereo MatchingCode3
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive CorrelationCode2
Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation GraphCode2
Neural Markov Random Field for Stereo MatchingCode2
BANet: Bilateral Aggregation Network for Mobile Stereo MatchingCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular BackbonesCode2
QuadTree Attention for Vision TransformersCode2
Iterative Geometry Encoding Volume for Stereo MatchingCode2
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised MatchingCode2
GeoMVSNet: Learning Multi-View Stereo With Geometry PerceptionCode2
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-ExpertsCode2
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical FlowCode2
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo MatchingCode2
Accurate and Efficient Stereo Matching via Attention Concatenation VolumeCode2
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