Deep Learning 3D Shape Surfaces Using Geometry Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9910)

Abstract

Surfaces serve as a natural parametrization to 3D shapes. Learning surfaces using convolutional neural networks (CNNs) is a challenging task. Current paradigms to tackle this challenge are to either adapt the convolutional filters to operate on surfaces, learn spectral descriptors defined by the Laplace-Beltrami operator, or to drop surfaces altogether in lieu of voxelized inputs. Here we adopt an approach of converting the 3D shape into a ‘geometry image’ so that standard CNNs can directly be used to learn 3D shapes. We qualitatively and quantitatively validate that creating geometry images using authalic parametrization on a spherical domain is suitable for robust learning of 3D shape surfaces. This spherically parameterized shape is then projected and cut to convert the original 3D shape into a flat and regular geometry image. We propose a way to implicitly learn the topology and structure of 3D shapes using geometry images encoded with suitable features. We show the efficacy of our approach to learn 3D shape surfaces for classification and retrieval tasks on non-rigid and rigid shape datasets.

Keywords

Deep learning 3D Shape Surfaces CNN Geometry images 

Supplementary material

419981_1_En_14_MOESM1_ESM.pdf (862 kb)
Supplementary material 1 (pdf 861 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Purdue UniversityWest LafayetteUSA
  2. 2.Beifang University of NationalitiesYinchuanChina

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