Geometric foundations for geometry processing of neural implicit representations of signed distance functions
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71324
comunitat-uji-handle3:10234/97526
comunitat-uji-handle4:
TFG-TFMMetadatos
Título
Geometric foundations for geometry processing of neural implicit representations of signed distance functionsAutoría
Tutor/Supervisor; Universidad.Departamento
Gual-Arnau, Ximo; Universitat Jaume I. Departament de MatemàtiquesFecha de publicación
2023-06-08Editor
Universitat Jaume IResumen
This work explores using neural networks to approximate Signed Distance Functions for representing
3D shapes. The work explains fundamental geometry concepts and their application
to these functions, as well as the ... [+]
This work explores using neural networks to approximate Signed Distance Functions for representing
3D shapes. The work explains fundamental geometry concepts and their application
to these functions, as well as the workings of neural networks as function approximators. By
connecting these concepts, it explains how neural networks can represent 3D shapes and how to
perform shape smoothing and sharpening on them. The objective of this work was to provide a
solid foundation for further exploration of this topic, with a focus on providing a mathematical
explanation of these techniques. Future research can explore other types of operations and
efficient ways of creating and manipulating these representations. [-]
Palabras clave / Materias
Descripción
Treball Final de Grau en Matemàtica Computacional. Codi: MT1054. Curs 2022-2023
Tipo de documento
info:eu-repo/semantics/bachelorThesisDerechos de acceso
info:eu-repo/semantics/openAccess