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Point Cloud Registration

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하위 태스크: 1
평가표: 24
보존된 평가 기록: 190

Point Cloud Registration is a fundamental problem in 3D computer vision and photogrammetry. Given several sets of points in different coordinate systems, the aim of registration is to find the transformation that best aligns all of them into a common coordinate system. Point Cloud Registration plays a significant role in many vision applications such as 3D model reconstruction, cultural heritage management, landslide monitoring and solar energy analysis.

Source: Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/514dna/llt

공식 코드

Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation

https://github.com/Alejandro-Galan/Global-PC-Large-Transformations-Registration

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Global Point Cloud Registration Network for Large Transformations

https://github.com/AliYoussef97/SuperPoint-PrP

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NeRF-Supervised Feature Point Detection and Description

https://github.com/AmnonDrory/BestBuddiesRegistration

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Best Buddies Registration for Point Clouds

https://github.com/Baio0/sliced_opt

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Sliced Optimal Partial Transport

https://github.com/DavidBoja/FAUST-partial

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Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration

https://github.com/GradientSpaces/LoopSplat

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LoopSplat: Loop Closure by Registering 3D Gaussian Splats

https://github.com/GuiyuZhao/SGOR

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SGOR: Outlier Removal by Leveraging Semantic and Geometric Information for Robust Point Cloud Registration

https://github.com/HpWang-whu/YOHO

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You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

https://github.com/JuanDuGit/DH3D

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DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization

https://github.com/LTU-RAI/FRAME

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FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration

https://github.com/Lilac-Lee/PointNetLK_Revisited

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PointNetLK Revisited

https://github.com/MIT-SPARK/TEASER-plusplus

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TEASER: Fast and Certifiable Point Cloud Registration

https://github.com/QingyongHu/SpinNet

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SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

https://github.com/SergioRAgostinho/just-a-spoonful

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(Just) A Spoonful of Refinements Helps the Registration Error Go Down

https://github.com/ShengyuH/OverlapPredator

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PREDATOR: Registration of 3D Point Clouds with Low Overlap

https://github.com/StephLin/FracGM

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FracGM: A Fast Fractional Programming Technique for Geman-McClure Robust Estimator

https://github.com/TejasAnvekar/Mahalanobis-k-NN

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Mahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations

https://github.com/TuSimple/LiDAR_SOT

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Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

https://github.com/UMich-CURLY/unified_cvo

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A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras

https://github.com/VinAIResearch/PointSWD

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Point-set Distances for Learning Representations of 3D Point Clouds

https://github.com/WHU-USI3DV/FreeReg

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FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators

https://github.com/WPC-WHU/GROR

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A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

https://github.com/WangYueFt/dcp

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Deep Closest Point: Learning Representations for Point Cloud Registration

https://github.com/WangYueFt/prnet

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PRNet: Self-Supervised Learning for Partial-to-Partial Registration

https://github.com/WanquanF/RMA-Net

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Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

https://github.com/XiangLiu0731/MFGNet

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Multi-Features Guidance Network for partial-to-partial point cloud registration

https://github.com/XiaoshuiHuang/IMFNet

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IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration

https://github.com/XiaoshuiHuang/fmr

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Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences

https://github.com/XuyangBai/D3Feat

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D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features

https://github.com/XuyangBai/PointDSC

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PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

https://github.com/YuePanEdward/GH-ICP

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Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration

https://github.com/ZhipengCai/Demo---Practical-optimal-registration-of-terrestrial-LiDAR-scan-pairs

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Practical optimal registration of terrestrial LiDAR scan pairs

https://github.com/acschaefer/ppe

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A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans

https://github.com/aevainc/Doppler-ICP

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DICP: Doppler Iterative Closest Point Algorithm

https://github.com/ai4ce/DeepMapping

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DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.