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Point cloud reconstruction from raw point cloud has been an important topicin computer graphics for decades, especially due to its high demand in modelingand rendering applications. An important way to solve this problem isestablishing a local geometry to fit the local curve. However, previous methodsbuild either a local plane or polynomial curve. Local plane brings the loss ofsharp feature and the boundary artefacts on open surface. Polynomial curve ishard to combine with neural network due to the local coordinate consistentproblem. To address this, we propose a novel polyhedral surface to representlocal surface. This method provides more flexible to represent sharp featureand surface boundary on open surface. It does not require any local coordinatesystem, which is important when introducing neural networks. Specifically, weuse normals to construct the polyhedral surface, including both dihedral andtrihedral surfaces using 2 and 3 normals, respectively. Our method achievesstate-of-the-art results on three commonly used datasets (ShapeNetCore, ABC,and ScanNet). Code will be released upon acceptance.

Polyhedral Surface: Self-supervised Point Cloud Reconstruction Based on Polyhedral Surface