Hadar Averbuch-Elor (Cornell-Tech)
Thursday, 4.2.2021, 16:30
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3D computer vision has significantly advanced over the past several decades, with modern algorithms successfully reconstructing entire urban cities. However, many questions remain unexplored, as geometric reasoning alone cannot fully infer the connections among images capturing different parts of the scene or semantic relationships between images captured at distant geographic locations.
In this talk, I will present an ongoing line of research that leverages powerful deep networks to address new and exciting problems in 3D vision. Considering a single 3D scene, we ask: Can we estimate the relative camera rotation between a pair of images in an extreme setting, where the images have little to no overlap? We address this seemingly impossible task by designing a neural network that can implicitly reason about hidden cues, such as vanishing points and direction of shadows. Expanding beyond a single scene, we jointly analyze dozens of 3D-augmented collections and connect them to a new domain: language. We demonstrate how a joint learned model that considers language, images, and 3D geometry can reason about the rich semantics associated with complex architectural landmarks. Finally, I will discuss several future directions.
Hadar Averbuch-Elor is a postdoctoral researcher at Cornell-Tech working with Prof. Noah Snavely. Her research interests lie in the intersection of computer graphics and computer vision. Currently, her research focuses on understanding and manipulating visual concepts by combining pixels with more structured modalities, including natural language and 3D geometry. She completed her PhD in Electrical Engineering at Tel-Aviv University where she was advised by Prof. Daniel Cohen-Or. She has a B.Sc. in Electrical Engineering from the Technion. She also held research positions at Facebook and Amazon AI. Hadar is the recipient of several awards including the Zuckerman Postdoctoral Scholar Fellowship and the Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences.