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Point Cloud Registration On Eth Trained On

Métriques

Feature Matching Recall

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
CGF0.202Learning Compact Geometric Features
DIP0.928Distinctive 3D local deep descriptors
GeDi0.982Learning general and distinctive 3D local deep descriptors for point cloud registration
FPFH0.221Fast Point Feature Histograms (FPFH) for 3D Registration
FCGF0.161Fully Convolutional Geometric Features
D3Feat-pred0.563D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
PerfectMatch0.790The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
Greedy Grid Search0.784Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
3DMatch0.1693DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Exhaustive Grid Search-Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark
FCGF+SC2-PCR-SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
YOHO-O-You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
FPFH+PointDSC-PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
FPFH+SC2-PCR-SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
GeoTransformer-GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
LMVD0.616End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
FCGF+PointDSC-PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
YOHO-C-You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
GO-ICP-Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
SpinNet0.928SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
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Point Cloud Registration On Eth Trained On | SOTA | HyperAI