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Stereo Matching
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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
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo Matching
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation
Multi-Label Stereo Matching for Transparent Scene Depth Estimation
M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data
Diving into the Fusion of Monocular Priors for Generalized Stereo Matching
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching
ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based Refinement
Consistency-aware Self-Training for Iterative-based Stereo Matching
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Distilling Stereo Networks for Performant and Efficient Leaner Networks
LeanStereo: A Leaner Backbone based Stereo Network
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching
RGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization
Stereo Any Video: Temporally Consistent Stereo Matching
BANet: Bilateral Aggregation Network for Mobile Stereo Matching
LightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy Images
SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking
FoundationStereo: Zero-Shot Stereo Matching
DEFOM-Stereo: Depth Foundation Model Based Stereo Matching
MonSter: Marry Monodepth to Stereo Unleashes Power
StereoGen: High-quality Stereo Image Generation from a Single Image
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching Transformer
Uncertainty Quantification in Stereo Matching
MobiFuse: A High-Precision On-device Depth Perception System with Multi-Data Fusion
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing
All-in-One: Transferring Vision Foundation Models into Stereo Matching
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data
Superpixel Cost Volume Excitation for Stereo Matching
Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.