Dr. Qianqian Wang (UC Berkeley): Learning to Perceive the 4D World  

Speaker: Dr. Qianqian Wang  (UC Berkeley):

Title: Learning to Perceive the 4D World  

Video: Click here to view

Abstract:Perceiving the 4D world (i.e., 3D space over time) from visual input is essential for human interaction with the physical environment. While computer vision has made remarkable progress in 3D scene understanding, much of it remains piecemeal — for example, focusing solely on static scenes or specific categories of dynamic objects. How can we model diverse dynamic scenes in the wild? How can we achieve online perception with human-like capabilities? In this talk, I will first discuss holistic scene representations that enable long-range motion estimation and 4D reconstruction. I will then introduce a unified learning-based framework for online dense 3D perception, which continuously refines scene understanding with new observations. I will conclude by discussing future directions and challenges in advancing visual intelligence.  

Bio: Qianqian Wang is an incoming Assistant Professor at Harvard University. She is currently with a stealth startup and was previously a postdoctoral researcher at UC Berkeley, working with Professors Angjoo Kanazawa and Alexei A. Efros. She received her Ph.D. in Computer Science from Cornell University in 2023, advised by Prof. Noah Snavely and Prof. Bharath Hariharan. Her research lies at the intersection of computer vision, computer graphics, and machine learning. She is a recipient of the ICCV Best Student Paper Award, CVPR Best Paper Honorable Mention, Cornell CS Dissertation Award, and EECS Rising Stars.