6 papers
HOSC: A Periodic Activation with Saturation Control for High-Fidelity Implicit Neural Representations
Michal Jan Wlodarczyk, Danzel Serrano, Przemyslaw Musialski
Periodic activations such as sine preserve high-frequency information in implicit neural representations (INRs) through their oscillatory structure, but often suffer from gradient…
A Finite Difference Approximation of Second Order Regularization of Neural-SDFs
Haotian Yin, Aleksander Plocharski, Michal Jan Wlodarczyk +1
We introduce a finite-difference framework for curvature regularization in neural signed distance field (SDF) learning. Existing approaches enforce curvature priors using full Hess…
Scheduling the Off-Diagonal Weingarten Loss of Neural SDFs for CAD Models
Haotian Yin, Przemyslaw Musialski
Neural signed distance functions (SDFs) have become a powerful representation for geometric reconstruction from point clouds, yet they often require both gradient- and curvature-ba…
Joint Neural SDF Reconstruction and Semantic Segmentation for CAD Models
Shen Fan, Przemyslaw Musialski
We propose a simple, data-efficient pipeline that augments an implicit reconstruction network based on neural SDF-based CAD parts with a part-segmentation head trained under PartFi…
FlatCAD: Fast Curvature Regularization of Neural SDFs for CAD Models
Haotian Yin, Aleksander Plocharski, Michal Jan Wlodarczyk +2
Neural signed-distance fields (SDFs) are a versatile backbone for neural geometry representation, but enforcing CAD-style developability usually requires Gaussian-curvature penalti…
Shrinking: Reconstruction of Parameterized Surfaces from Signed Distance Fields
Haotian Yin, Przemyslaw Musialski
We propose a novel method for reconstructing explicit parameterized surfaces from Signed Distance Fields (SDFs), a widely used implicit neural representation (INR) for 3D surfaces.…