Creating Generative Models from Range Images

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Creating Generative Models from Range Images
We describe a new approach for creating concise high-level generative models from range images or other approximate representations of real objects. Using data from a variety of acquisition techniques 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 parameter 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 technique for optimizing a model by refining its constituent curves. We demonstrate our approach for model recovery using both real and synthetic data and several generative model hierarchies...
Ravi Ramamoorthi, James Arvo
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Authors Ravi Ramamoorthi, James Arvo
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