THESIS DEFENSE: Deep Learning on Geometry Representations

Speaker: Dmitriy Smirnov , CSAIL MIT

Date: Thursday, April 21, 2022

Time: 10:00 AM to 11:00 AM Note: all times are in the Eastern Time Zone

Public: Yes

Location: STAR Conference Room (32-D463)

Event Type: Thesis Defense

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Host: Justin Solomon, CSAIL MIT

Contact: Mieke Moran, 617-253-5817,

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Reminder Subject: TALK: THESIS DEFENSE: Deep Learning on Geometry Representations

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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 talk, I will show how revisiting conventional approaches can yield deep learning pipelines and inductive biases that are directly compatible with common geometry representations. In particular, I will discuss works on applying learning to triangle meshes, CAD-style parametric primitives, sprites, and hybrid explicit/implicit shapes with boundaries.

Short Bio:
Dmitriy (Dima) Smirnov is a PhD student in the Geometric Data Processing group at MIT, advised by Professor Justin Solomon. His research lies on the intersection of computer graphics, geometry processing, and deep learning. Dima is an NSF Graduate Research Fellow and has done internships at Adobe Research and Pixar Research.

Thesis Committee: Justin Solomon (MIT), William T. Freeman (MIT), Mikhail Bessmeltsev (Université de Montréal)

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Created by Mieke Moran Email at Tuesday, April 19, 2022 at 3:13 PM.