Compressed Representation of 3D Models for 3D Printing

In this work we tackle the issue of large file sizes in disk of volume data representations of 3D models when used for 3D printing purposes. In the pipeline of 3D printing software, the representation, in disk, of the model go from the initial representation, which normally is a polygonal mesh, to v...

Descripción completa

Detalles Bibliográficos
Autor: Ballesteros Brandão, João Hugo
Tipo de recurso: tesis de maestría
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/344884
Acceso en línea:https://hdl.handle.net/2117/344884
Access Level:acceso abierto
Palabra clave:Three-dimensional printing
Data compression (Computer science)
3D Printing
Voxelization
Lossless Compression
3D Model
Computer Graphics
Run Length Encoding
Impressió 3D
Dades -- Compressió (Informàtica)
Àrees temàtiques de la UPC::Informàtica
Descripción
Sumario:In this work we tackle the issue of large file sizes in disk of volume data representations of 3D models when used for 3D printing purposes. In the pipeline of 3D printing software, the representation, in disk, of the model go from the initial representation, which normally is a polygonal mesh, to volume data and later into instructions for the printer. Each of these stages has a cost in terms of size of disk and memory usage, and with large models with high resolutions, the size of the representations can become unfeasible to save and the memory usage may be too large. For that reason, a better encoding of the representation of volume data is necessary, while maintaining the efficiency of the processing of slices and not impacting or reducing the memory usage of the creating of such encoding. We describe several methods with different configurations utilised to reduce the size of the representation in disk, such as Run Length encoding and Modulus Encoding, while also compressing them with known lossless compression algorithms such as LZMA, used by 7Zip, and DEFLATE, used by GZip. We utilise several models to show that our results greatly reduce the size of the representation without impacting any other stage of the pipeline, and therefore providing a better alternative to current market techniques.