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%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.