HyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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HyperAI초신경

Point Cloud Registration On 3Dmatch At Least 1

평가 지표

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

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
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
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