Weight-Aware Implicit Geometry Reconstruction with Curvature-Guided Sampling
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Neural surface implicit representations offer numerous advantages, including the ability to easily modify topology and surface resolution. However, reconstructing implicit geometry representation with only limited known data is challenging. In this paper, we present an approach that effectively interpolates and extrapolates within training points, generating additional training data to reconstruct a surface with superior qualitative and quantitative results. We also introduce a technique that efficiently calculates differentiable geometric properties, i.e., mean and Gaussian curvatures, to enhance the sampling process during training. Additionally, we propose a weight-aware implicit neural representation that not only streamlines surface extraction but also extend to non-closed surfaces by depicting non-closed areas as locally degenerated patches, thereby mitigating the drawbacks of the previous assumption in implicit neural representations.
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