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Point Cloud Registration On Kitti Trained On

Métriques

Success Rate

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	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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Point Cloud Registration On Kitti Trained On | SOTA | HyperAI