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- W4382246206 abstract "The existing mesh reconstruction methods rely on the complete point cloud input. However, the point cloud collected in the actual environment is always the partial point cloud of an object/scene. The direct mesh reconstruction from the partial point cloud is a challenging problem but there is currently no end-to-end method for mesh reconstruction from partial point clouds. To solve this challenge, this paper proposes an end-to-end mesh reconstruction from the partial point cloud method based on continuous implicit function. Specifically, the continuous implicit function of a complete point cloud is learned by combining the local and global features of a partial point cloud. Then we sample a point cloud from the continuous implicit function and reconstruct the mesh by leveraging the deformation network. A loss function based on the point cloud normal vector is proposed to further optimize mesh reconstruction. Experiments on the ShapeNet-55 dataset show that the loss of our method in three different incomplete degrees of the point cloud is reduced by 35%, 38%, and 38% respectively compared with NMF, with an average reduction of 38%." @default.
- W4382246206 created "2023-06-28" @default.
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- W4382246206 date "2023-06-27" @default.
- W4382246206 modified "2023-09-24" @default.
- W4382246206 title "End-to-end mesh reconstruction from partial point cloud based on continuous implicit function" @default.
- W4382246206 doi "https://doi.org/10.1117/12.2680457" @default.
- W4382246206 hasPublicationYear "2023" @default.
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