Speaker: Ashutosh Saxena 
 
Affiliation: Stanford University, CS

Title: Robotic Grasping and Depth Perception: Learning 3D Models from a Single Image

 
Abstract:
We present an algorithm to convert 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 use a Markov Random Field (MRF) to 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 fairly accurate 3D models.
To convert your own image of an outdoor scene, landscape, etc. to a 3D model, please visit:
http://make3d.stanford.edu
 
We also apply our methods to robotics applications: (a) obstacle avoidance for autonomously 
driving a small electric car, and (b) robot manipulation, where we develop vision-based 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.
 
Joint work with Prof. Andrew Y. Ng
 
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