Learning to Perceive the 4D World

Zoom link for remote attendees

Meeting ID: 999 4631 3276 Passcode: 123123

Abstract: Perceiving the dynamic 3D (4D) world 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 general 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 spatial intelligence and beyond.

Bio: Qianqian Wang is a postdoc at UC Berkeley working with Prof. Angjoo Kanazawa and Prof. 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. She is a recipient of ICCV Best Student Paper Award, Google PhD fellowship and EECS rising stars.