Accueil Actualités Articles de recherche récents Tutoriels Ensembles de données Wiki SOTA Modèles LLM Classement GPU Événements

Français

Point Cloud Registration On 3Dmatch Benchmark

Métriques

Feature Matching Recall

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Feature Matching Recall	Paper Title	Repository
D3Feat-Pred	95.8	D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features	-
3DSmoothNet	94.7	The Perfect Match: 3D Point Cloud Matching with Smoothed Densities	-
PPF-FoldNet	71.8	PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors	-
D3Feat-rand	95.3	D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features	-
IMFNet	98.6	IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration	-
FCGF + RANSAC	85	Fully Convolutional Geometric Features
MS-SVConv	98.4	3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning	-
DIP	94.8	Distinctive 3D local deep descriptors	-
FPFH + RANSAC	44.2	Fast Point Feature Histograms (FPFH) for 3D Registration
SpinNet (no code published as of Dec 15 2020)	97.6	SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration	-
3DMatch + RANSAC	66.8	3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions	-
GeDi (no code published as of May 27 2021)	97.9	Learning general and distinctive 3D local deep descriptors for point cloud registration	-
DIP + Point-TnT	96.8	Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition	-
LMVD	97.5	End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds	-
PPFNet	62.3	PPFNet: Global Context Aware Local Features for Robust 3D Point Matching	-

0 of 15 row(s) selected.

Point Cloud Registration On 3Dmatch Benchmark | SOTA | HyperAI