High-Order Continuous Geometrical Validity
June 06, 2024 Β· Declared Dead Β· π ACM Transactions on Graphics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Federico Sichetti, Zizhou Huang, Marco Attene, Denis Zorin, Enrico Puppo, Daniele Panozzo
arXiv ID
2406.03756
Category
cs.CG: Computational Geometry
Cross-listed
cs.GR
Citations
2
Venue
ACM Transactions on Graphics
Last Checked
3 months ago
Abstract
We propose a conservative algorithm to test the geometrical validity of simplicial (triangles, tetrahedra), tensor product (quadrilaterals, hexahedra), and mixed (prisms) elements of arbitrary polynomial order as they deform over a piecewise-linear trajectory. Our algorithm uses a combination of adaptive BΓ©zier refinement and bisection search to determine if, when, and where the Jacobian determinant of an element's polynomial geometric map becomes negative in the transition from one configuration to another. Unlike previous approaches, our method preserves its properties also when implemented using floating point arithmetic: This feature comes at a small additional runtime cost compared to existing inexact methods, making it a drop-in replacement for current validity tests, while providing superior robustness and generality. To prove the practical effectiveness of our algorithm, we demonstrate its use in a high-order Incremental Potential Contact (IPC) elastodynamic simulator, and we experimentally show that it prevents invalid, simulation-breaking configurations that would otherwise occur using inexact methods, without the need for manual parameter tuning.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computational Geometry
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Dynamic Planar Convex Hull
R.I.P.
π»
Ghosted
TEMPO: Feature-Endowed TeichmΓΌller Extremal Mappings of Point Clouds
R.I.P.
π»
Ghosted
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization
R.I.P.
π»
Ghosted
Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal
R.I.P.
π»
Ghosted
Momen(e)t: Flavor the Moments in Learning to Classify Shapes
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted