https://github.com/yoichiro0406/cascading_feature_extraction
공식 코드Cascading Feature Extraction for Fast Point Cloud Registration
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/yorklyb/LiDAR-SFM
공식 코드L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration
https://github.com/yuvalh9/umeregrobust
공식 코드UMERegRobust -- Universal Manifold Embedding Compatible Features for Robust Point Cloud Registration
https://github.com/yxzhang15/pcr
공식 코드Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomy
https://github.com/zengyiming-eamon/idea-net
공식 코드IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.