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3D Object Completion via Class-Conditional Generative Adversarial Network

  • Yu-Chieh Chen
  • Daniel Stanley Tan
  • Wen-Huang Cheng
  • Kai-Lung HuaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)

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.

Keywords

Object reconstruction Shape completion Generative adversarial network Object classification 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yu-Chieh Chen
    • 1
  • Daniel Stanley Tan
    • 1
  • Wen-Huang Cheng
    • 2
  • Kai-Lung Hua
    • 1
    Email author
  1. 1.Department of CSIENational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Department of EENational Chiao Tung UniversityHsinchuTaiwan

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