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dc.contributor.authorSavarese, Silvioen_US
dc.date.accessioned2013-12-02T20:51:10Z
dc.date.available2013-12-02T20:51:10Z
dc.date.issued2013-11-15
dc.identifier.urihttp://hdl.handle.net/1853/49749
dc.descriptionSilvio Savarese is an assistant professor of computer science at Stanford University. He earned his Ph.D. in Electrical Engineering from the California Institute of Technology in 2005 and was a Beckman Institute Fellow at the University of Illinois at Urbana-Champaign from 2005-2008. He joined Stanford in 2013 after being assistant and then associate professor (with tenure) at the University of Michigan, Ann Arbor. from 2008 to 2013. Savarese's research interests include computer vision, object recognition and scene understanding, shape representation and reconstruction, human activity recognition and visual psychophysics. He is recipient of several awards, including the James R. Croes Medal in 2013, a TRW Automotive Endowed Research Award in 2012, an NSF Career Award in 2011 and Google Research Award in 2010. In 2002, Savarese received the Walker von Brimer Award for outstanding research initiative.en_US
dc.descriptionPresented on November 15, 2013 from 12:00 pm - 1:00 pm in the TSRB Auditorium.en_US
dc.descriptionRuntime: 56:10 minutes.en_US
dc.description.abstractWhen we look at an environment such as a coffee shop, we don't just recognize the objects in isolation, but rather perceive a rich scenery of the 3D space, its objects and all the relations among them. This allows us to effortlessly navigate through the environment, or to interact and manipulate objects in the scene with amazing precision. The past several decades of computer vision research have, on the other hand, addressed the problems of 2D object recognition and 3D space reconstruction as two independent problems. Tremendous progress has been made in both areas. However, while methods for object recognition attempt to describe the scene as a list of class labels, they often make mistakes due to the lack of a coherent understanding of the 3D spatial structure. Similarly, methods for scene 3D modeling can produce accurate metric reconstructions but cannot put the reconstructed scene into a semantically useful form. A major line of work from my group in recent years has been to design intelligent visual models that understand the 3D world by integrating 2D and 3D cues, inspired by what humans do. In this talk I will introduce a novel paradigm whereby objects and 3D space are modeled in a joint fashion to achieve a coherent and rich interpretation of the environment. I will start by giving an overview of our research for detecting objects and determining their geometric properties such as 3D location, pose or shape. Then, I will demonstrate that these detection methods play a critical role for modeling the interplay between objects and space, which in turn, enable simultaneous semantic reasoning and 3D scene reconstruction. I will conclude this talk by demonstrating that our novel paradigm for scene understanding is potentially transformative in application areas such as autonomous or assisted navigation, robotics, automatic 3D modeling of urban environments and surveillance.en_US
dc.format.extent56:10 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesIRIM Seminar Seriesen_US
dc.subjectRoboticsen_US
dc.subjectImagingen_US
dc.subject3D modelingen_US
dc.subject3D spaceen_US
dc.subjectObject recognitionen_US
dc.subjectAutonomous navigationen_US
dc.subjectAssisted navigationen_US
dc.titlePerceiving the 3D World from Imagesen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameStanford Universityen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US


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  • IRIM Seminar Series [116]
    Each semester a core seminar series is announced featuring guest speakers from around the world and from varying backgrounds in robotics.

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