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Morphing and Sampling Network for Dense Point Cloud Completion | | Minghua Liu
; Lu Sheng
; Sheng Yang
; Jing Shao
; Shi-Min Hu
; | | Date: |
30 Nov 2019 | | Abstract: | 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). | | Source: | arXiv, 1912.0280 | | Services: | Forum | Review | PDF | Favorites | |
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