https://github.com/hkust-aerial-robotics/pagor
공식 코드Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap
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/hmgoforth/PointNetLK
공식 코드PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/horizon-research/pointcloud-pipeline
공식 코드Tigris: Architecture and Algorithms for 3D Perception in Point Clouds
https://github.com/huangjh-pub/synorim
공식 코드Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization
https://github.com/hui-design/hd2reg
공식 코드HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration
https://github.com/humanpose1/MS-SVConv
공식 코드3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning
https://github.com/hxwork/HybridReg_PyTorch
공식 코드HybridReg: Robust 3D Point Cloud Registration with Hybrid Motions
https://github.com/iit-DLSLab/SANDRO
공식 코드SANDRO: a Robust Solver with a Splitting Strategy for Point Cloud Registration
https://github.com/intellhave/SDRSAC
공식 코드https://github.com/irmvlab/regformer
공식 코드https://github.com/ispc-lab/HRegNet
공식 코드HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
https://github.com/ispc-lab/PointINet
공식 코드https://github.com/ispc-lab/RSKDD-Net
공식 코드RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
https://github.com/ispc-lab/neuralpci
공식 코드NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation
https://github.com/itailang/SampleNet
공식 코드https://github.com/itanhe/deepbbs
공식 코드https://github.com/ivanxie416/cmignet
공식 코드Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
https://github.com/ivanxie416/ifnet
공식 코드Iterative Feedback Network for Unsupervised Point Cloud Registration
https://github.com/jiahaowork/idam
공식 코드https://github.com/jiang-hb/cemnet
공식 코드Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration
https://github.com/jianqingzheng/path_planning_for_FEVAR
공식 코드https://github.com/jiaweihe1996/GMTracker
공식 코드Learnable Graph Matching: A Practical Paradigm for Data Association
https://github.com/joshnguyen99/partialot
공식 코드On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods
https://github.com/karim416/LoGDesc
공식 코드LoGDesc: Local geometric features aggregation for robust point cloud registration
https://github.com/kezheng1204/integer
공식 코드Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
https://github.com/laka-3dv/ml-semreg
공식 코드ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistency
https://github.com/langnatalie/DeepUME
공식 코드DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration
https://github.com/leisun-98/daniel
공식 코드https://github.com/leisun-98/tivm-master
공식 코드https://github.com/lijx10/DeepI2P
공식 코드DeepI2P: Image-to-Point Cloud Registration via Deep Classification
https://github.com/lilin-hitcrt/bevnet
공식 코드A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation
https://github.com/linchengweiii/CFReg
공식 코드Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representations
https://github.com/liuquan98/apr
공식 코드APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction
https://github.com/liuquan98/eyoc
공식 코드https://github.com/liuquan98/gcl
공식 코드Density-invariant Features for Distant Point Cloud Registration
https://github.com/liyih/hybridpoint
공식 코드HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.