Morphing and Sampling Network for Dense Point Cloud Completion

Understand

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.