“Neuralize” geometry processing pipeline

Abstract

Fueled by the proliferation of consumer-level 3D acquisition devices and the growing accessibility of shape modeling applications for ordinary users, there is a tremendous need for automatic geometry processing algorithms that perform robustly even under incomplete and distorted data. This talk demonstrates how each step of the geometry processing pipeline can be automated and, more importantly, strengthened by utilizing neural networks to leverage consistencies and high-level semantic priors from data.

Yifan Wang

(also publish as Wang Yifan)

Research Scientist at Adobe