SPSR - Silvia Sellán

Talking Papers Podcast

In this episode of the Talking Papers Podcast, I hosted Silvia Sellán. We had a great chat about her paper "Stochastic Poisson Surface Reconstruction”, published in SIGGRAPH Asia 2022.

In this paper, they take on the task of surface reconstruction with a probabilistic twist. They take the well-known Poisson Surface reconstruction algorithm and generalize it to give it a full statistical formalism. Essentially their method quantifies the uncertainty of surface reconstruction from a point cloud. Instead of outputting an implicit function, they represent the shape as a modified Gaussian process. This unique perspective and interpretation enables conducting statistical queries, for example, given a point, is it on the surface? is it inside the shape?
Silvia is currently a PhD student at the University of Toronto. Her research focus is on computer graphics and geometric processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow and the winner of the 2021 UoFT FAS Deans Doctoral excellence scholarship. I have been following Silvia's work for a while and since I have some work on surface reconstruction when SPSR came out, I knew I wanted to host her on the podcast (and gladly she agreed). Silvia is currently looking for postdoc and faculty positions to start in the fall of 2024. I am really looking forward to seeing which institute snatches her.

In our conversation, I particularly liked her explanation of Gaussian Processes with the example "How long does it take my supervisor to answer an email as a function of the time of day the email was sent", You can't read that in any book. But also, we took an unexpected pause from the usual episode structure to discuss the question of "papers" as a medium for disseminating research. Don't miss it.

AUTHORS
Silvia Sellán, Alec Jacobson
ABSTRACT
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.

RELATED PAPERS
📚Poisson Surface Reconstruction

📚Geometric Priors for Gaussian Process Implicit Surfaces

📚Gaussian processes for machine learning
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