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Point Cloud Registration On 3Dlomatch 10 30

Metrics

Recall ( correspondence RMSE below 0.2)

Results

Performance results of various models on this benchmark

		Paper Title	Repository
GeoTransformer - P2PNet	74	Geometric Transformer for Fast and Robust Point Cloud Registration
NgeNet	71.9	Leveraging Inlier Correspondences Proportion for Point Cloud Registration
REGTR	64.8	REGTR: End-to-end Point Cloud Correspondences with Transformers
Predator-1k	62.5	PREDATOR: Registration of 3D Point Clouds with Low Overlap
Predator-5k	59.8	PREDATOR: Registration of 3D Point Clouds with Low Overlap
PCAM (reported in REGTR)	54.9	PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
DGR (reported in REGTR)	48.7	Deep Global Registration
FCGF (reported in PREDATOR)	40.1	Fully Convolutional Geometric Features	-
D3Feat (reported in PREDATOR)	37.2	D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
3DSN (reported in PREDATOR)	33	The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
Predator-NR	24	PREDATOR: Registration of 3D Point Clouds with Low Overlap
OMNet (reported in REGTR)	8.4	OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration

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Point Cloud Registration On 3Dlomatch 10 30 | SOTA | HyperAI