February 17, 2010 : Ashutosh Saxena

Cornell University

Make3D: Single Image Depth Perception and its applications to Robotics

We consider the problem of converting standard digital pictures into 3D models. This is a challenging problem, since an image is formed by a projection of the 3D scene onto two dimensions, thus losing the depth information. We take a supervised learning approach to this problem, and model the scene depth as a function of the image features. We show that, even on unstructured scenes of a large variety of environments, our algorithm is frequently able to recover accurate 3D models. We then apply our methods to robotics applications: (a) obstacle avoidance for autonomously driving a small electric car at high speeds through cluttered environments, and (b) robot manipulation, where we develop learning algorithms for grasping novel objects. This enables our robot to perform tasks such as open new doors, clear up cluttered tables, and unload items from a dishwasher.

Bio:

Ashutosh Saxena is an assistant professor in computer science department at Cornell University. His research interests include machine learning, computer vision and robotics. He received his MS in 2006 and Ph.D. in 2009 from Stanford University, and his B.Tech. in 2004 from Indian Institute of Technology (IIT) Kanpur. He has worked in Bose Corporation, CSIRO (Australia) and Microsoft in the past, and has founded Zunavision in 2008.

Ashutosh has developed Make3D (http://make3d.stanford.edu), an algorithm that converts a single photograph into a 3D model. Tens of thousands of users used this technology to convert their pictures to 3D. He has also developed algorithms that enable robots to perform household chores such as unload items from a dishwasher. His work has received substantial amount of attention in popular press, including the front-page of New York Times, BBC, ABC, New Scientist and Wired Magazine. He has won best paper awards in 3DRR and IEEE ACE. He was also a recipient of National Talent Scholar award in India.

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