Deep Learning on Geometry Representations

While deep learning has been successfully applied to many tasks in computer graphics and vision, standard learning architectures often operate on shape representations that are dense and regular, like pixel or voxel grids. On the other hand, decades of computer graphics and geometry processing research have resulted in specialized algorithms and tools that use representations without such regular structure. In this thesis, we revisit conventional approaches in graphics in geometry to propose deep learning pipelines and inductive biases that are directly compatible with common geometry representations, without relying on simple uniform structure.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Smirnov, Dmitriy

Advisor dc:contributor.advisor

• Solomon, Justin

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144568

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144568