Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network

Weini Zhang, Linwei Chen, Zhangyue Xiong, Yu Zang, Jonathan Li, Lei Zhao

Research output: Contribution to journalArticlepeer-review


As one of the most important features for human perception, contours are widely used in many graphics and mapping applications. However, for large outdoor scale point clouds, contour extraction is considerably challenging due to the huge, unstructured and irregular point space, thus leading to massive failure for existing approaches. In this paper, to generate contours consistent with human perception for outdoor scenes, we propose, for the first time, 3D guided multi-conditional GAN (3D-GMcGAN), a deep neural network based contour extraction network for large scale point clouds. Specifically, two ideas are proposed to enable the network to learn the distributions of labeled samples. First, a parametric space based framework is proposed via a novel similarity measurement of two parametric models. Such a framework significantly compresses the huge point data space, thus making it much easier for the network to “remember” target distribution. Second, to prevent network loss in the huge solution space, a guided learning framework is designed to assist finding the target contour distribution via an initial guidance. To evaluate the effectiveness of the pro-posed network, we open-sourced the first, to our knowledge, dataset for large scale point cloud with contour annotation information. Experimental results demonstrate that 3D-GMcGAN efficiently generates contours for the data with more than ten million points (about several minutes), while avoiding ad hoc stages or parameters. Also, the proposed framework produces minimal outliers and pseudo-contours, as suggested by comparisons with the state-of-the-art approaches.

Original languageEnglish (US)
Pages (from-to)97-105
Number of pages9
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - Jun 2020


  • Contour extraction
  • Large-scale point cloud
  • Multi-conditional GAN

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences

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