We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings.

Melzi, S., Spezialetti, R., Tombari, F., Bronstein, M., Di Stefano, L., Rodolà, E. (2019). GFrames: gradient-based local reference frame for 3d shape matching. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp.4624-4633). IEEE COMPUTER SOC [10.1109/CVPR.2019.00476].

GFrames: gradient-based local reference frame for 3d shape matching

Melzi, S;
2019

Abstract

We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings.
paper
3D from Multiview and Sensors; Categorization; Low-level Vision; Motion and Tracking; Recognition: Detection; Retrieval;
English
CVPR 2019 - Conference on Computer Vision and Pattern Recognition
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
978-172813293-8
2019
2019-June
4624
4633
8953995
reserved
Melzi, S., Spezialetti, R., Tombari, F., Bronstein, M., Di Stefano, L., Rodolà, E. (2019). GFrames: gradient-based local reference frame for 3d shape matching. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp.4624-4633). IEEE COMPUTER SOC [10.1109/CVPR.2019.00476].
File in questo prodotto:
File Dimensione Formato  
Melzi_GFrames_2019.pdf

Solo gestori archivio

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 2.62 MB
Formato Adobe PDF
2.62 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/350452
Citazioni
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 8
Social impact