Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions. A common approach to the construction of directional fields from data relies on complex optimization procedures, which are usually poorly formalizable, require a considerable computational effort, and do not transfer across applications. In this work, we propose a deep learning-based approach and study the expressive power and generalization ability.
CITATION STYLE
Taktasheva, M., Matveev, A., Artemov, A., & Burnaev, E. (2019). Learning to approximate directional fields defined over 2D planes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11832 LNCS, pp. 367–374). Springer. https://doi.org/10.1007/978-3-030-37334-4_33
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