Planar Shape Detection at Structural Scales - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

Planar Shape Detection at Structural Scales

Résumé

Interpreting 3D data such as point clouds or surface meshes depends heavily on the scale of observation. Yet, existing algorithms for shape detection rely on trial-and-error parameter tunings to output configurations representative of a structural scale. We present a framework to automatically extract a set of representations that capture the shape and structure of man-made objects at different key abstraction levels. A shape-collapsing process first generates a fine-to-coarse sequence of shape representations by exploiting local planarity. This sequence is then analyzed to identify significant geometric variations between successive representations through a supervised energy minimization. Our framework is flexible enough to learn how to detect both existing structural formalisms such as the CityGML Levels Of Details, and expert-specified levels of abstraction. Experiments on different input data and classes of man-made objects, as well as comparisons with existing shape detection methods, illustrate the strengths of our approach in terms of efficiency and flexibility.
Fichier principal
Vignette du fichier
cvpr2018.pdf (11.4 Mo) Télécharger le fichier
Vignette du fichier
cvpr18_hao.png (185.02 Ko) Télécharger le fichier
cvpr18_hao.jpg (48.08 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image

Dates et versions

hal-01741650 , version 1 (23-03-2018)

Identifiants

  • HAL Id : hal-01741650 , version 1

Citer

Hao Fang, Florent Lafarge, Mathieu Desbrun. Planar Shape Detection at Structural Scales. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, Salt Lake City, United States. ⟨hal-01741650⟩
508 Consultations
803 Téléchargements

Partager

Gmail Facebook X LinkedIn More