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Creating Generative Models from Range Images Ravi Ramamoorthi
 

Summary: Creating Generative Models from Range Images
Ravi Ramamoorthi
Stanford University
ravir@graphics.stanford.edu
James Arvo
California Institute of Technology
arvo@cs.caltech.edu
Abstract
We describe a new approach for creating concise high-level gener-
ative models from range images or other approximate representa-
tions of real objects. Using data from a variety of acquisition tech-
niques and a user-defined class of models, our method produces a
compact object representation that is intuitive and easy to edit. The
algorithm has two inter-related phases: recognition, which chooses
an appropriate model within a user-specified hierarchy, and param-
eter estimation, which adjusts the model to best fit the data. Since
the approach is model-based, it is relatively insensitive to noise and
missing data. We describe practical heuristics for automatically
making tradeoffs between simplicity and accuracy to select the best
model in a given hierarchy. We also describe a general and efficient

  

Source: Arvo, Jim - Departments of Information and Computer Science & Electrical and Computer Engineering, University of California, Irvine
Columbia University, Department of Computer Science, Computer Graphics Group
O'Brien, James F. - Department of Electrical Engineering and Computer Sciences, University of California at Berkeley

 

Collections: Computer Technologies and Information Sciences