11 citations · 15 across the 11 of their papers we have counts for
5 papers · 1 filter
Residual Primitive Fitting of 3D Shapes with SuperFrusta
Aditya Ganeshan, Matheus Gadelha, Thibault Groueix +5
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…
Splat and Replace: 3D Reconstruction with Repetitive Elements
Nicolás Violante, Andreas Meuleman, Alban Gauthier +3
We leverage repetitive elements in 3D scenes to improve novel view synthesis. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly improved novel view synthe…
MagicClay: Sculpting Meshes With Generative Neural Fields
Amir Barda, Vladimir G. Kim, Noam Aigerman +2
The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fu…
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
Noam Aigerman, Kunal Gupta, Vladimir G. Kim +3
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogen…