https://github.com/514dna/llt
공식 코드Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation
Papers with Code 아카이브
3D Point Cloud Interpolation
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Point cloud interpolation is a fundamental problem for 3D computer vision. Given a low temporal resolution (frame rate) point cloud sequence, the target of interpolation is to generate a smooth point cloud sequence with high temporal resolution (frame rate).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Alejandro-Galan/Global-PC-Large-Transformations-Registration
공식 코드Global Point Cloud Registration Network for Large Transformations
https://github.com/AliYoussef97/SuperPoint-PrP
공식 코드https://github.com/AmnonDrory/BestBuddiesRegistration
공식 코드https://github.com/Baio0/sliced_opt
공식 코드https://github.com/DavidBoja/FAUST-partial
공식 코드Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
https://github.com/GradientSpaces/LoopSplat
공식 코드https://github.com/GuiyuZhao/SGOR
공식 코드https://github.com/HpWang-whu/YOHO
공식 코드You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
https://github.com/JuanDuGit/DH3D
공식 코드DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization
https://github.com/LTU-RAI/FRAME
공식 코드FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
https://github.com/Lilac-Lee/PointNetLK_Revisited
공식 코드https://github.com/MIT-SPARK/TEASER-plusplus
공식 코드https://github.com/QingyongHu/SpinNet
공식 코드SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
https://github.com/SergioRAgostinho/just-a-spoonful
공식 코드(Just) A Spoonful of Refinements Helps the Registration Error Go Down
https://github.com/ShengyuH/OverlapPredator
공식 코드https://github.com/StephLin/FracGM
공식 코드FracGM: A Fast Fractional Programming Technique for Geman-McClure Robust Estimator
https://github.com/TejasAnvekar/Mahalanobis-k-NN
공식 코드Mahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations
https://github.com/TuSimple/LiDAR_SOT
공식 코드Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
https://github.com/UMich-CURLY/unified_cvo
공식 코드A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras
https://github.com/VinAIResearch/PointSWD
공식 코드Point-set Distances for Learning Representations of 3D Point Clouds
https://github.com/WHU-USI3DV/FreeReg
공식 코드https://github.com/WPC-WHU/GROR
공식 코드https://github.com/WangYueFt/dcp
공식 코드Deep Closest Point: Learning Representations for Point Cloud Registration
https://github.com/WangYueFt/prnet
공식 코드PRNet: Self-Supervised Learning for Partial-to-Partial Registration
https://github.com/WanquanF/RMA-Net
공식 코드Recurrent Multi-view Alignment Network for Unsupervised Surface Registration
https://github.com/XiangLiu0731/MFGNet
공식 코드Multi-Features Guidance Network for partial-to-partial point cloud registration
https://github.com/XiaoshuiHuang/IMFNet
공식 코드IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration
https://github.com/XiaoshuiHuang/fmr
공식 코드https://github.com/XuyangBai/D3Feat
공식 코드D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
https://github.com/XuyangBai/PointDSC
공식 코드PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
https://github.com/YuePanEdward/GH-ICP
공식 코드https://github.com/ZhipengCai/Demo---Practical-optimal-registration-of-terrestrial-LiDAR-scan-pairs
공식 코드Practical optimal registration of terrestrial LiDAR scan pairs
https://github.com/acschaefer/ppe
공식 코드A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans
https://github.com/aevainc/Doppler-ICP
공식 코드https://github.com/ai4ce/DeepMapping
공식 코드DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.