Dynamic Graph CNN for Learning on Point Clouds

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point…

• NS
  National Science Foundation

  Awards: W911NF-12-R-0011, IIS-1838071, 1838071
• SI
  Skolkovo Institute of Science and Technology
• AF
  Air Force Office of Scientific Research

  Award: FA9550-19-1-0319
• AR
  Army Research Office

  Awards: W911NF-12-R-0011, W911NF