6 papers
StyleFields: Multi-Scale AdaIN-Modulated Implicit SDFs for Coarse-to-Fine 3D Shape Reconstruction and Editing
Ehsan Garaaghaji, Nicolas Talabot, Pascal Fua +1
We introduce StyleFields, a DeepSDF-based architecture for high-fidelity 3D reconstruction that enables controllable geometric style mixing: the coarse structure of one object can…
Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models
Emilien Seiler, Nicolas Talabot, Yingxuan You +2
Optimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term manifo…
PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
Nicolas Talabot, Olivier Clerc, Arda Cinar Demirtas +4
Accurate 3D shape representation is essential in engineering applications such as design, optimization, and simulation. In practice, engineering workflows require structured, part-…
Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields
Corentin Dumery, Aoxiang Fan, Ren Li +2
Radiance Fields have become a powerful tool for modeling 3D scenes from multiple images. However, they remain difficult to segment into semantically meaningful regions. Some method…
Neural Surface Detection for Unsigned Distance Fields
Federico Stella, Nicolas Talabot, Hieu Le +1
Extracting surfaces from Signed Distance Fields (SDFs) can be accomplished using traditional algorithms, such as Marching Cubes. However, since they rely on sign flips across the s…
HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation
Subeesh Vasu, Nicolas Talabot, Artem Lukoianov +3
Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives a…