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 spatial intelligence.
Bio: Qianqian Wang is a postdoctoral researcher 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. 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, Google PhD Fellowship, and EECS Rising Stars.
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