Defesa de Dissertação de Mestrado do aluno Daniel Luca Alves

Defesa de Dissertação de Mestrado do aluno Daniel Luca Alves.

Título da dissertação: Graph-Based Clustering In Deep Feature Space for Shape Matching

Resumo: Engineering projects rely on complex 3D CAD models throughout their life cycle. These 3D models consist of millions of geometries that impose challenges in storage, transmission, and rendering. Previous works have successfully employed shape matching techniques based on deep learning to reduce the memory required by these 3D models. In this work, we propose a graph-based algorithm that improves unsupervised clustering in deep feature space. This approach greatly improves shape matching accuracy and results in even lower memory requirements for the 3D models. In a labeled dataset, our method achieves a 95% model reduction, outperforming previous unsupervised techniques that achieved 87% and almost reaching the 97% reduction from a fully supervised approach. In an unlabeled dataset, our method achieves an average model reduction of 87% versus an average reduction of 77% from previous unsupervised techniques.

Orientador: Prof. Dr. Waldemar Celes Filho

Co-orientador: Prof. Dr Paulo Ivson Netto Santos

Banca: Prof. Dr. Alberto Barbosa Raposo | Prof. Dr. Anselmo Cardoso de Paiva | Prof. Dr. Lucas Caracas de Figueiredo


Assista a defesa pelo link: https://puc-rio.zoom.us/j/94119149852?pwd=dFpZTkR2Rll1NHZoM3pORmVicXRoZz09