Creating Generative Models from Range Images
Abstract
We describe a new approach for creating concise high-level generative models from range images or other methods of obtaining approximate point clouds. Using a variety of acquisition techniques and a user-defined class of models, our method produces a compact and intuitive object description that is robust to noise and is 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 fit the data as closely as possible. We give a simple method for automatically making tradeoffs between simplicity and accuracy to determine the best model within a given hierarchy. We also describe general techniques to optimize a specific generative model that include methods for curve-fitting, and which exploit sparsity. Using a few simple generative hierarchies, that subsume many of the models previously used in computer vision, we demonstrate our approach for model recovery on real and synthetic data.
Citation
Ravi Ramamoorthi and James Arvo. "Creating Generative Models from Range Images". In SIGGRAPH '99, pages 195–204, 1999.