Signed Distance Function Computation from an Implicit Surface
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 16 Apr 2021.
Builds on
12 of 25 references.
References · 12
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
- 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
- 2020Implicit Geometric Regularization for Learning Shapescited by 707
- 2017The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problemscited by 581
- 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
- 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
- 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
- 2020MetaSDF: Meta-learning Signed Distance Functionscited by 214
- 2020SALD: Sign Agnostic Learning with Derivativescited by 115
- 2019Controlling Neural Level Setscited by 76
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Citing papers per year: 2023: 1.
1 paper.