https://github.com/yanivw12/gs2mesh
공식 코드GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Views
Papers with Code 아카이브
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/yaochengtang/decnet
공식 코드https://github.com/yihui-he/epipolar-transformers
공식 코드https://github.com/youmi-zym/temporalstereo
공식 코드TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network
https://github.com/yuhuaxu/monostereofusion
공식 코드Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light
https://github.com/zhuofeng-wu/qpdnet
공식 코드https://github.com/zhuxing0/SR-Stereov1-DAPE
공식 코드SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo Matching
https://github.com/ziweiwwang/shef
공식 코드https://github.com/ztsrxh/DVANet
공식 코드Depth-aware Volume Attention for Texture-less Stereo Matching
https://github.com/ztsrxh/roadbev
공식 코드https://github.com/zwxu064/RANP
공식 코드RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
https://github.com/zyangchen/hart
공식 코드Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching Transformer
https://github.com/zyangchen/mocha-stereo
공식 코드MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
https://github.com/zzzkkkyyy/padenet
공식 코드Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor Scenes
https://github.com/3dcvdeveloper/bgnet
https://github.com/AvrilCheng/LidarStereoNet
https://github.com/ChelseaGH/sidewalk_prototype_AI_Hub
https://github.com/FloralZhao/MVPSNet
https://github.com/MindCode-4/code-5/tree/main/yolos
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
https://github.com/Mirmix/ddlmvs
https://github.com/UCI-ISA-Lab/MultiHeadDepth-HomoDepth
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
https://github.com/andrewlstewart/StereoNet_PyTorch
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
https://github.com/apchenstu/mvsnerf
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
https://github.com/bartn8/vppstereo
https://github.com/brainless-afk/Binary_Stereo_Matching
https://github.com/bruno-31/groupsc
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding
https://github.com/erezposner/Fast_Dense_Feature_Extraction
https://github.com/gengshan-y/high-res-stereo
https://github.com/gidariss/DRR_struct_pred
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling
https://github.com/hz-ants/cascade-mvsnet
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
https://github.com/ibaiGorordo/CREStereo-Pytorch
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
https://github.com/ibaiGorordo/HITNET-Stereo-Depth-estimation
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
https://github.com/ibaiGorordo/ONNX-ACVNet-Stereo-Depth-Estimation
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
https://github.com/ibaiGorordo/ONNX-CREStereo-Depth-Estimation
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
https://github.com/ibaiGorordo/ONNX-FastACVNet-Depth-Estimation
Accurate and Efficient Stereo Matching via Attention Concatenation Volume
https://github.com/ibaiGorordo/ONNX-HITNET-Stereo-Depth-estimation
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.