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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hanlingsgjk/unifiedgeneralization
공식 코드https://github.com/haofeixu/aanet
공식 코드AANet: Adaptive Aggregation Network for Efficient Stereo Matching
https://github.com/hkbu-hpml/easnet
공식 코드EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching
https://github.com/hongzhidu/SRHNet
공식 코드SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching
https://github.com/horizon-research/ism-algorithm
공식 코드https://github.com/horizon-research/systolic-array-dataflow-optimizer
공식 코드https://github.com/hyu-cvlab/omnimvs-pytorch
공식 코드OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
https://github.com/insta360-research-team/defom-stereo
공식 코드https://github.com/isl-cv/chitransformer
공식 코드https://github.com/jiaw-z/dkt-stereo
공식 코드https://github.com/jiaw-z/fcstereo
공식 코드Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective
https://github.com/jiawei-mo/scale_optimization
공식 코드Extending Monocular Visual Odometry to Stereo Camera Systems by Scale Optimization
https://github.com/jiaxiZeng/Temporally-Consistent-Stereo-Matching
공식 코드https://github.com/jimmy19991222/elfnet
공식 코드https://github.com/jingweisong/bdis-slam
공식 코드BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery
https://github.com/jingweisong/bdis-v2
공식 코드BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching
https://github.com/junda24/monster
공식 코드https://github.com/jytime/DICL-Flow
공식 코드Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
https://github.com/jzbontar/mc-cnn
공식 코드Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
https://github.com/jzhangbs/DSM
공식 코드Learning Stereo Matchability in Disparity Regression Networks
https://github.com/jzhangbs/MVSDF
공식 코드Learning Signed Distance Field for Multi-view Surface Reconstruction
https://github.com/kbatsos/CBMV
공식 코드CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
https://github.com/lawy623/SVS
공식 코드https://github.com/lelimite4444/BridgeDepthFlow
공식 코드Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
https://github.com/liuyuan-pal/NeuRay
공식 코드https://github.com/lly00412/sednet
공식 코드https://github.com/lulinzhang/sps-nerf
공식 코드SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite Images
https://github.com/m2219/dcvsmnet
공식 코드https://github.com/m2219/esmstereo
공식 코드ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo Matching
https://github.com/macrohuang1993/ESNet
공식 코드https://github.com/magicboomliu/goat
공식 코드Global Occlusion-Aware Transformer for Robust Stereo Matching
https://github.com/mattpoggi/guided-stereo
공식 코드https://github.com/megvii-research/crestereo
공식 코드Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
https://github.com/menandro/vfs
공식 코드Real-Time Variational Fisheye Stereo without Rectification and Undistortion
https://github.com/miaojief/mc-stereo
공식 코드MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.