Rising Stars Seminar Series • Uncertain Surface Reconstruction

Please note: This seminar will take place in DC 1304 and virtually over Zoom.

Silvia Sellán, PhD candidate
Department of Computer Science, University of Toronto

We propose a method to introduce uncertainty to the surface reconstruction problem. Specifically, we introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm for recovering shapes from 3D point clouds. Instead of outputting an implicit function, we represent the reconstructed shape as a modified Gaussian Process, which allows us to conduct statistical queries (e.g., the likelihood of a point in space being on the surface or inside a solid). We show that this perspective improves PSR’s integration into the online scanning process, broadens its application realm, and opens the door to other lines of research such as applying task-specific priors.

Bio: Silvia is a fourth-year Computer Science PhD student at the University of Toronto. She is advised by Alec Jacobson and working in Computer Graphics and Geometry Processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow and the winner of the 2021 University of Toronto Arts & Science Dean’s Doctoral Excellence Scholarship. She has interned twice at Adobe Research and twice at the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH. She is currently looking to survey potential future postdoc and faculty positions, starting Fall 2024.

To attend this rising stars seminar series presentation in person, please go to DC 1304. You can also attend virtually using Zoom at https://uwaterloo.zoom.us/j/98928115536.

To receive the passcode, please email Joe Petrik.