Abstract
Subdivision is an important and widely used technique for obtaining dense meshes from coarse control (triangular) meshes for modelling and animation purposes. Most subdivision algorithms use engineered features (subdivision
rules). Recently, neural subdivision successfully applied machine learning to the subdivision of a triangular mesh. It uses a simple neural network to learn an optimal vertex positioning during a subdivision step. We propose an extension to the neural subdivision algorithm that introduces explicit curvature information
into the network. This makes a larger amount of relevant information accessible which allows the network to yield better results. We demonstrate that this modification yields significant improvement over the original algorithm, in terms of both Hausdorff distance and mean squared error.
rules). Recently, neural subdivision successfully applied machine learning to the subdivision of a triangular mesh. It uses a simple neural network to learn an optimal vertex positioning during a subdivision step. We propose an extension to the neural subdivision algorithm that introduces explicit curvature information
into the network. This makes a larger amount of relevant information accessible which allows the network to yield better results. We demonstrate that this modification yields significant improvement over the original algorithm, in terms of both Hausdorff distance and mean squared error.
| Original language | English |
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| Publication status | Published - 31-May-2023 |
| Event | The 36th International Conference on Computer Animation and Social Agents - Limassol, Cyprus Duration: 29-May-2023 → 31-May-2023 |
Conference
| Conference | The 36th International Conference on Computer Animation and Social Agents |
|---|---|
| Abbreviated title | CASA 2023 |
| Country/Territory | Cyprus |
| City | Limassol |
| Period | 29/05/2023 → 31/05/2023 |