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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

Model Name
RE (all)
Recall (0.3m, 15 degrees)
TE (all)
Paper TitleRepository
PCAM-Sparse (All post-processing)8.992.40.23PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds-
GeoTransformer-95-Geometric Transformer for Fast and Robust Point Cloud Registration-
RANSAC-2M-66.1-Fast Point Feature Histograms (FPFH) for 3D Registration
DCP-3.22-Deep Closest Point: Learning Representations for Point Cloud Registration-
ICP (P2Plane)-6.59-Open3D: A Modern Library for 3D Data Processing-
Super4PCS-21.6-Super 4PCS Fast Global Pointcloud Registration via Smart Indexing
Go-ICP-22.9-Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration-
PointNetLK-1.61-PointNetLK: Robust & Efficient Point Cloud Registration using PointNet-
FGR-42.7-Fast Global Registration
Exhaustive Grid Search-84.11-Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark
ICP (P2Point)-6.04-Open3D: A Modern Library for 3D Data Processing-
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-
NgeNet4.93295.00.155Leveraging Inlier Correspondences Proportion for Point Cloud Registration-
0 of 14 row(s) selected.
Point Cloud Registration On 3Dmatch At Least 1 | SOTA | HyperAI