Robust Point Cloud Registration via Local-to-Global Geometric Feature Aggregation - Robotique, Perception et Interaction pour le Biomédical
Poster De Conférence Année : 2024

Robust Point Cloud Registration via Local-to-Global Geometric Feature Aggregation

Résumé

his paper presents a hybrid descriptor for 3D point matching and point cloud registration. It combines local geometric properties with learning-based feature propagation to describe the neighborhood structure of each point. The architecture first extracts prior geometric information by computing the planarity, anisotropy, and omnivariance of each point using Principal Component Analysis (PCA). This information is augmented by a descriptor based on normal vectors estimated by a triangle-based shape construction.
Fichier principal
Vignette du fichier
iros_abstract.pdf (138.69 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04722222 , version 1 (04-10-2024)

Identifiants

  • HAL Id : hal-04722222 , version 1

Citer

Karim Slimani, Brahim Tamadazte, Catherine Achard. Robust Point Cloud Registration via Local-to-Global Geometric Feature Aggregation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), Oct 2024, Abu Dhabi, United Arab Emirates. ⟨hal-04722222⟩
20 Consultations
11 Téléchargements

Partager

More