https://github.com/yuvalh9/umeregrobust
공식 코드UMERegRobust -- Universal Manifold Embedding Compatible Features for Robust Point Cloud Registration
Papers with Code 아카이브
Point Cloud Registration
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yxzhang15/pcr
공식 코드Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomy
https://github.com/zexinyang/GlobalMatch
공식 코드https://github.com/zgojcic/3DSmoothNet
공식 코드The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
https://github.com/zgojcic/3D_multiview_reg
공식 코드https://github.com/zhangxy0517/3d-registration-with-maximal-cliques
공식 코드https://github.com/zhanhz/krf
공식 코드PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation
https://github.com/zhaoyiww/coff
공식 코드Cross-modal feature fusion for robust point cloud registration with ambiguous geometry
https://github.com/zhengxi-git/phase-slam
공식 코드https://github.com/zhichen902/amr
공식 코드Adaptive Multi-step Refinement Network for Robust Point Cloud Registration
https://github.com/zhichen902/detarnet
공식 코드DetarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration
https://github.com/zhichen902/sc2-pcr
공식 코드SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
https://github.com/zhileichen99/utopic
공식 코드UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
https://github.com/zhiyuanyu134/miretr
공식 코드https://github.com/zhulf0804/ROPNet
공식 코드Point Cloud Registration using Representative Overlapping Points
https://github.com/zhulf0804/ngenet
공식 코드Leveraging Inlier Correspondences Proportion for Point Cloud Registration
https://github.com/zikai1/graphreg
공식 코드GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing
https://github.com/zikai1/oareg
공식 코드Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised Neural Deformation Correntropy
https://github.com/zlynpu/3dfmnet
공식 코드3D Focusing-and-Matching Network for Multi-Instance Point Cloud Registration
https://github.com/zxrzju/superline3d
공식 코드SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
https://gitlab.com/nct_tso_public/c2p-net
공식 코드https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/ApolloScapeAuto/dataset-api
https://github.com/CyberPegasus/KUKA_Catch_Project
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
https://github.com/DavidBoja/exhaustive-grid-search
https://github.com/HSarham/Open3D-0.9.0
https://github.com/HaoXu-Work/FINet
FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration
https://github.com/IntelVCL/Open3D
https://github.com/KimSinjeong/CS492H_DeepClosestPoint
Deep Closest Point: Learning Representations for Point Cloud Registration
https://github.com/LONG-9621/Match_SmoothNet
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
https://github.com/LONG-9621/open3d_2
https://github.com/MIT-SPARK/CertifiablyRobustPerception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.