https://github.com/LONG-9621/Match_SmoothNet
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
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/LONG-9621/open3d_2
https://github.com/MIT-SPARK/CertifiablyRobustPerception
https://github.com/MStypulkowski/CIF
Representing Point Clouds with Generative Conditional Invertible Flow Networks
https://github.com/MindCode-4/code-14/tree/main/LIE-IQA
Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet_34
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/NomitRwt/DiplomaProject
https://github.com/Pacific-cyber/KUKA_Catch_Project
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
https://github.com/STORM-IRIT/OpenGR
https://github.com/StrikerCC/Teaser-plusplus-testing
https://github.com/XuyangBai/D3Feat.pytorch
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
https://github.com/XuyangBai/PPF-FoldNet
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
https://github.com/bigeyesung/test1
https://github.com/changliu816/CV-paper-review
https://github.com/chrischoy/FCGF
https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code8/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code9/tree/main/PVNet
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/code-implementation1/Code9/tree/main/PointNet
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/contiliosung/open3D
https://github.com/craigleili/3DLocalMultiViewDesc
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
https://github.com/dahliau/DPDist
PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/dengzhi-ustc/a-robust-registration-loss
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
https://github.com/edikkesh/Open3D
https://github.com/esteimle/teaser
https://github.com/fpthink/stnet
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
https://github.com/fpthink/v2b
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
https://github.com/ftdlyc/KCNet_Pytorch
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
https://github.com/grossjohannes/AlignNet-3D
AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects
https://github.com/haoyu94/roitr
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
https://github.com/hkust-aerial-robotics/g3reg
Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap
https://github.com/hui-design/aand
PARE-Net: Position-Aware Rotation-Equivariant Networks for Robust Point Cloud Registration
https://github.com/intel-isl/Open3D
https://github.com/intel-isl/Open3D-ML
https://github.com/isl-org/FastGlobalRegistration
https://github.com/isl-org/Open3D
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.