Morphing and Sampling Network for Dense Point Cloud Completion


  • Minghua Liu UC San Diego
  • Lu Sheng Beihang University
  • Sheng Yang Tsinghua University
  • Jing Shao Sensetime
  • Shi-Min Hu Tsinghua University



3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).




How to Cite

Liu, M., Sheng, L., Yang, S., Shao, J., & Hu, S.-M. (2020). Morphing and Sampling Network for Dense Point Cloud Completion. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11596-11603.



AAAI Technical Track: Vision