Search Results for author:
Found 5 papers, 2 papers with code
DeepMesh: Differentiable Iso-Surface Extraction
Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with respect to the underlying deep implicit field.
Masksembles for Uncertainty Estimation
Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
MeshSDF: Differentiable Iso-Surface Extraction
Unfortunately, these methods are often not suitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field.
NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler
Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points.
Kullback-Leibler Proximal Variational Inference
Secondly, we use the proximal framework to derive efficient variational algorithms for non-conjugate models.
