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Point Cloud Registration On Kitti Trained On

Metrics

Success Rate

Results

Performance results of various models on this benchmark

Model Name	Success Rate	Paper Title	Repository
FCGF+PointDSC	96.76	PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
GeoTransformer	67.93	GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
YOHO-O	81.44	You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
GeDi	98.92	Learning general and distinctive 3D local deep descriptors for point cloud registration
FCGF	24.19	Fully Convolutional Geometric Features
YOHO-C	82.16	You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
SpinNet	81.44	SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
D3Feat-pred	36.76	D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
Greedy Grid Search	90.27	Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
FCGF+SC2-PCR	97.66	SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration	-
Exhaustive Grid Search	94.95	Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark
Predator	41.20	PREDATOR: Registration of 3D Point Clouds with Low Overlap
FPFH+PointDSC	94.05	PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
DIP	93.51	Distinctive 3D local deep descriptors

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