34 citations · 88 across the 9 of their papers we have counts for
11 papers
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…
DeepMesh: Differentiable Iso-Surface Extraction
Benoit Guillard, Edoardo Remelli, Artem Lukoianov +4
Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary…
Masksembles for Uncertainty Estimation
Nikita Durasov, Timur Bagautdinov, Pierre Baque +1
Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being on…
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter +4
Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary…
NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler
Edoardo Remelli, Pierre Baque, Pascal Fua
Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points. Furthermore, they typically require…
Geodesic Convolutional Shape Optimization
Pierre Baqué, Edoardo Remelli, François Fleuret +1
Aerodynamic shape optimization has many industrial applications. Existing methods, however, are so computationally demanding that typical engineering practices are to either simply…