2 citations · 2 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Beyond Blur: A Fluid Perspective on Generative Diffusion Models
Grzegorz Gruszczynski, Jakub Meixner, Michal Jan Wlodarczyk +1
We propose a novel PDE-driven corruption process for generative image synthesis based on advection-diffusion processes which generalizes existing PDE-based approaches. Our forward…
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…
Pro-DG: Procedural Diffusion Guidance for Architectural Facade Generation
Aleksander Plocharski, Jan Swidzinski, Przemyslaw Musialski
We use hierarchical procedural rules for the generation of control maps within the stable diffusion framework to produce photo-realistic architectural facade images. Starting from…