High-Order Differential Regularizing Implicit Surface Representation of Point Cloud

Reconstructing surfaces from diverse raw data in computer graphics poses an enduring challenge. While recent methods deploy neural networks for direct implicit surface reconstruction, they struggle with degraded raw data quality, especially in edge regions. To address this, we advocate for employing high-order total generalized variation (TGV) as a regularization term for implicit surface representation. Acknowledging the non-trivial nature of extending typical image processing methods to implicit surfaces, we present an end-to-end trainable network framework for TGV in implicit surface reconstruction. This approach preserves sharp features, enhances smooth region recovery, and minimizes artificial artifacts. Simultaneously, we address the challenge of increased computational complexity associated with current algorithms by predicting it directly through an implicit neural function. Experimental results demonstrate the efficacy of our technical approach, providing a promising solution for robust implicit surface reconstruction.