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Point Cloud Registration On 3Dmatch At Least 1

Metrics

RE (all)
Recall (0.3m, 15 degrees)
TE (all)

Results

Performance results of various models on this benchmark

Paper TitleRepository
GeoTransformer-95-Geometric Transformer for Fast and Robust Point Cloud Registration
NgeNet4.93295.00.155Leveraging Inlier Correspondences Proportion for Point Cloud Registration
PCAM-Sparse (All post-processing)8.992.40.23PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
PCAM-Soft (All post-processing)9.891.30.24PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
DGR (RE (all), TE(all) are reported in PCAM)9.591.30.25Deep Global Registration
Exhaustive Grid Search-84.11-Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark-
RANSAC-2M-66.1-Fast Point Feature Histograms (FPFH) for 3D Registration-
FGR-42.7-Fast Global Registration-
Go-ICP-22.9-Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration-
Super4PCS-21.6-Super 4PCS Fast Global Pointcloud Registration via Smart Indexing-
ICP (P2Plane)-6.59-Open3D: A Modern Library for 3D Data Processing
ICP (P2Point)-6.04-Open3D: A Modern Library for 3D Data Processing
DCP-3.22-Deep Closest Point: Learning Representations for Point Cloud Registration
PointNetLK-1.61-PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
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Point Cloud Registration On 3Dmatch At Least 1 | SOTA | HyperAI