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https://doi.org/10.1109/cvpr.2...
Article . 2019 . Peer-reviewed
License: STM Policy #29
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GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching

Authors: S. Melzi; R. Spezialetti; F. Tombari; M. M. Bronstein; L. Di Stefano; E. Rodolà;

GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching

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.

Keywords

3D from Multiview and Sensors; Categorization; Low-level Vision; Motion and Tracking; Recognition: Detection; Retrieval, 3D from Multiview and Sensors; Categorization; Low-level Vision; Motion and Tracking; Recognition: Detection; Retrieval;, shape analysis, shape matching, local reference frame, shape descriptor. gradient, local reference frame; 3D meshes; shape analysis

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    popularity
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    Top 10%
    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Average
Top 10%
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