Signed Distance Function Computation from an Implicit Surface
arXiv:2104.08057
Abstract
We describe in this short note a technique to convert an implicit surface into a Signed Distance Function (SDF) while exactly preserving the zero level-set of the implicit. The proposed approach relies on embedding the input implicit in the final layer of a neural network, which is trained to minimize a loss function characterizing the SDF.
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References in corpus (5)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- MetaSDF: Meta-learning Signed Distance Functions
- On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
- Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes
- Neural Unsigned Distance Fields for Implicit Function Learning