Object Synthesis by Learning Part Geometry with Surface and Volumetric Representations

Sangpil Kim, Hyung gun Chi, Karthik Ramani

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

We propose a conditional generative model, named Part Geometry Network (PG-Net), which synthesizes realistic objects and can be used as a robust feature descriptor for object reconstruction and classification. Surface and volumetric representations of objects have complementary properties of three-dimensional objects. Combining these modalities is more informative than using one modality alone. Therefore, PG-Net utilizes complementary properties of surface and volumetric representations by estimating curvature, surface area, and occupancy in voxel grids of objects with a single decoder as a multi-task learning. Objects are combinations of multiple parts, and therefore part geometry (PG) is essential to synthesize each part of the objects. PG-Net employs a part identifier to learn the part geometry. Additionally, we augmented a dataset by interpolating individual functional parts such as wings of an airplane, which helps learning part geometry and finding local/global minima of PG-Net. To demonstrate the capability of learning object representations of PG-Net, we performed object reconstruction and classification tasks on two standard large-scale datasets. PG-Net outperformed the state-of-the-art methods in object synthesis, classification, and reconstruction in a large margin.

Original languageEnglish
Article number102932
JournalCAD Computer Aided Design
Volume130
DOIs
Publication statusPublished - 2021 Jan
Externally publishedYes

Bibliographical note

Funding Information:
This work was partially supported by the national science foundation under grants FW-HTF 1839971 and OIA 1937036 . We also thank the Donald W. Feddersen endowment for re-initiating this research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agency.

Publisher Copyright:
© 2020 Elsevier Ltd

Keywords

  • Conditional generative model
  • Deep learning
  • Multi-task learning
  • Object synthesis

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Industrial and Manufacturing Engineering

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