Geometric attention for prediction of differential properties in 3d point clouds

Albert Matveev, Alexey Artemov, Denis Zorin, Evgeny Burnaev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Estimation of differential geometric quantities in discrete 3D data representations is one of the crucial steps in the geometry processing pipeline. Specifically, estimating normals and sharp feature lines from raw point clouds helps improve meshing quality and allows us to use more precise surface reconstruction techniques. When designing a learnable approach to such problems, the main difficulty is selecting neighborhoods in a point cloud and incorporating geometric relations between the points. In this study, we present a geometric attention mechanism that can provide such properties in a learnable fashion. We establish the usefulness of the proposed technique with several experiments on the prediction of normal vectors and the extraction of feature lines.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks in Pattern Recognition - 9th IAPR TC3 Workshop, ANNPR 2020, Proceedings
EditorsFrank-Peter Schilling, Thilo Stadelmann
PublisherSpringer Science and Business Media Deutschland GmbH
Pages113-124
Number of pages12
ISBN (Print)9783030583088
DOIs
StatePublished - 2020
Event9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020 - Winterthur, Switzerland
Duration: Sep 2 2020Sep 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12294 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020
CountrySwitzerland
CityWinterthur
Period9/2/209/4/20

Keywords

  • 3D computer vision
  • 3D point clouds
  • Attention

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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