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Found 3 papers, 0 papers with code
Neural ShDF: Reviving an Efficient and Consistent Mesh Segmentation Method
Using our predicted SDF values, we can inject the resulting structure into a graph-cut algorithm to generate an efficient and robust mesh segmentation while considerably reducing the required computation times.
Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids
The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions.
GraphSeam: Supervised Graph Learning Framework for Semantic UV Mapping
In this work, we use the power of supervised GNNs for the first time to propose a fully automated UV mapping framework that enables users to replicate their desired seam styles while reducing distortion and seam length.
Graph Learning
Graphics
