Point Cloud Registration On Eth Trained On

Metrics

Feature Matching Recall

Results

Performance results of various models on this benchmark

		Paper Title
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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Point Cloud Registration On Eth Trained On

Metrics

Results