Morphing and Sampling Network for Dense Point Cloud Completion
This work addresses the challenge of generating high-fidelity, dense point clouds from partial inputs for applications in computer vision and robotics, representing an incremental advancement in the field.
The paper tackles the problem of 3D point cloud completion by proposing a two-stage method that predicts a coarse-grained point cloud and merges it with the input using a novel sampling algorithm, resulting in improved performance over existing methods as measured by Earth Mover's Distance and Chamfer Distance.
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).