CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating

Authors

  • Ximing Yang Fudan University
  • Yuan Wu Fudan University Peng Cheng Laboratory
  • Kaiyi Zhang Fudan University
  • Cheng Jin Fudan University Peng Cheng Laboratory

Keywords:

3D Computer Vision

Abstract

Generative Adversarial Networks (GAN) are good at generating variant samples of complex data distributions. Generating a sample with certain properties is one of the major tasks in the real-world application of GANs. In this paper, we propose a novel generative adversarial network to generate 3D point clouds from random latent codes, named Controllable Point Cloud Generative Adversarial Network(CPCGAN). A two-stage GAN framework is utilized in CPCGAN and a sparse point cloud containing major structural information is extracted as the middle-level information between the two stages. With their help, CPCGAN has the ability to control the generated structure and generate 3D point clouds with semantic labels for points. Experimental results demonstrate that the proposed CPCGAN outperforms state-of-the-art point cloud GANs.

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Published

2021-05-18

How to Cite

Yang, X., Wu, Y., Zhang, K., & Jin, C. (2021). CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 3154-3162. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16425

Issue

Section

AAAI Technical Track on Computer Vision III