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.

GFrames: gradient-based local reference frame for 3d shape matching / Melzi, S.; Spezialetti, R.; Tombari, F.; Bronstein, M. M.; Di Stefano, L.; Rodolà, E.. - 2019-June:(2019), pp. 4624-4633. (Intervento presentato al convegno Proc. Int’l Conference on Computer Vision and Pattern Recognition (CVPR) tenutosi a Long Beach; United States) [10.1109/CVPR.2019.00476].

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

S. Melzi;E. Rodolà
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.
2019
Proc. Int’l Conference on Computer Vision and Pattern Recognition (CVPR)
local reference frame; 3D meshes; shape analysis
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
GFrames: gradient-based local reference frame for 3d shape matching / Melzi, S.; Spezialetti, R.; Tombari, F.; Bronstein, M. M.; Di Stefano, L.; Rodolà, E.. - 2019-June:(2019), pp. 4624-4633. (Intervento presentato al convegno Proc. Int’l Conference on Computer Vision and Pattern Recognition (CVPR) tenutosi a Long Beach; United States) [10.1109/CVPR.2019.00476].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1360134
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