3D object completion via class-conditional generative adversarial network

Yu Chieh Chen, Daniel Stanley Tan, Wen-Huang Cheng, Kai Lung Hua*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Many robotic tasks require accurate shape models in order to properly grasp or interact with objects. However, it is often the case that sensors produce incomplete 3D models due to several factors such as occlusion or sensor noise. To address this problem, we propose a semi-supervised method that can recover the complete the shape of a broken or incomplete 3D object model. We formulated a hybrid of 3D variational autoencoder (VAE) and generative adversarial network (GAN) to recover the complete voxelized 3D object. Furthermore, we incorporated a separate classifier in the GAN framework, making it a three player game instead of two which helps stabilize the training of the GAN as well as guides the shape completion process to follow the object class labels. Our experiments show that our model produces 3D object reconstructions with high-similarity to the ground truth and outperforms several baselines in both quantitative and qualitative evaluations.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 25th International Conference, MMM 2019, Proceedings
EditorsBenoit Huet, Ioannis Kompatsiaris, Stefanos Vrochidis, Vasileios Mezaris, Wen-Huang Cheng, Cathal Gurrin
PublisherSpringer Verlag
Pages54-66
Number of pages13
ISBN (Print)9783030057152
DOIs
StatePublished - 1 Jan 2019
Event25th International Conference on MultiMedia Modeling, MMM 2019 - Thessaloniki, Greece
Duration: 8 Jan 201911 Jan 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11296 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on MultiMedia Modeling, MMM 2019
CountryGreece
CityThessaloniki
Period8/01/1911/01/19

Keywords

  • Generative adversarial network
  • Object classification
  • Object reconstruction
  • Shape completion

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