Point Cloud Registration On Eth Trained On

평가 지표

Feature Matching Recall

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Feature Matching Recall	Paper Title	Repository
CGF	0.202	Learning Compact Geometric Features	-
DIP	0.928	Distinctive 3D local deep descriptors	-
GeDi	0.982	Learning general and distinctive 3D local deep descriptors for point cloud registration	-
FPFH	0.221	Fast Point Feature Histograms (FPFH) for 3D Registration
FCGF	0.161	Fully Convolutional Geometric Features
D3Feat-pred	0.563	D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features	-
PerfectMatch	0.790	The Perfect Match: 3D Point Cloud Matching with Smoothed Densities	-
Greedy Grid Search	0.784	Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration	-
3DMatch	0.169	3DMatch: 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	-
LMVD	0.616	End-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	-
SpinNet	0.928	SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration	-

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