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PoNQ: a Neural QEM-based Mesh Representation
Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applications.
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D surfaces remains a challenge.
Dynamic Upsampling of Smoke through Dictionary-based Learning
We propose a novel dictionary-based neural network which learns both a fast evaluation of sparse patch encoding and a dictionary of corresponding coarse and fine patches from a sequence of example simulations computed with any numerical solver.
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Parallel Transport Unfolding: A Connection-based Manifold Learning Approach
Our new geometric procedure exhibits the same strong resilience to noise as one of the staples of manifold learning, the Isomap algorithm, as it also exploits all pairwise geodesic distances to compute a low-dimensional embedding.
Planar Shape Detection at Structural Scales
Interpreting 3D data such as point clouds or surface meshes depends heavily on the scale of observation.
