10 citations · 14 across the 7 of their papers we have counts for
7 papers
Monte Carlo Steklov Operators for Large-Scale Geometry Processing in the Wild
Arman Maesumi, Tanish Makadia, Aruna Anderson +3
Intrinsic methods fill the default toolbox for geometry processing on meshes. Intrinsic operators, in particular the Laplacian, underlie methods that require invariance to isometry…
Residual Primitive Fitting of 3D Shapes with SuperFrusta
Aditya Ganeshan, Matheus Gadelha, Thibault Groueix +6
We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persistent trade-off between reconstruction f…
PoissonNet: A Local-Global Approach for Learning on Surfaces
Arman Maesumi, Tanish Makadia, Thibault Groueix +3
Many network architectures exist for learning on meshes, yet their constructions entail delicate trade-offs between difficulty learning high-frequency features, insufficient recept…
Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy
Aditya Ganeshan, Thibault Groueix, Paul Guerrero +3
Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often programmatic: s…
One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Arman Maesumi, Dylan Hu, Krishi Saripalli +4
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different typ…
Explorable Mesh Deformation Subspaces from Unstructured Generative Models
Arman Maesumi, Paul Guerrero, Vladimir G. Kim +4
Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can,…
The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference
R. Kenny Jones, Aalia Habib, Rana Hanocka +1
We propose the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign fine-grained semantic labels to regions of a 3D shape. NGSP solves this problem via MAP infer…