Creating Generative Models from Range Images
Creators: name: Ramamoorthi, Ravi
Publisher: name: California Institute of Technology
Year: 1998

description: We describe a new approach for creating concise high-level generative models from one or more approximate range images. Using simple acquisition techniques and a user-defined class of models, our method produces a simple and intuitive object description that is relatively insensitive to noise and is easy to manipulate and edit. The algorithm has two inter-related phases -- recognition, which chooses an appropriate model within a given hierarchy, and parameter estimation, which adjusts the model to fit the data. We give a simple method for automatically making tradeoffs between simplicity and accuracy to determine the best model. We also describe general techniques to optimize a specific generative model. In particular, we address the problem of creating a suitable objective function that is sufficiently continuous for use with finite-difference based optimization techniques. Our technique for model recovery and subsequent manipulation and editing is demonstrated on real objects -- a spoon, bowl, ladle, and cup -- using a simple tree of possible generative models. We believe that higher-level model representations are extremely important, and their recovery for actual objects is a fertile area of research towards which this thesis is a step. However, our work is preliminary and there are currently several limitations. The user is required to create a model hierarchy (and supply methods to provide an initial guess for model parameters within this hierarchy); the use of a large pre-defined class of models can help alleviate this problem. Further, we have demonstrated our technique on only a simple tree of generative models. While our approach is fairly general, a real system would require a tree that is significantly larger. Our methods work only where the entire object can be accurately represented as a single generative model; future work could use constructive solid geometry operations on simple generative models to represent more complicated shapes. We believe that many of the above limitations can be addressed in future work, allowing us to easily acquire and process three-dimensional shape in a simple, intuitive and efficient manner.
description: Firstly, I would like to thank my advisor, Jim Arvo, for being a constant source of ideas and inspiration,
and for creating many of the illustrations in the text. My former advisor, Al Barr, deserves special
mention for guiding me during my first steps in research, and for many preliminary discussions on
the subject matter contained in these pages. Jean-Yves Bouguet provided many helpful hints for
data acquisition, and made many insightful comments that have significantly improved the quality of
exposition.
This thesis represents only one part of the wonderful experience I've had these past 4 years as an
undergraduate at Caltech, and I would like to take this opportunity to thank other faculty members
who have mentored me during the course of research projects—Brad Werner, and K. Mani Chandy.
Tom Tombrello deserves special thanks for the Phil program, a model for undergraduate research.
I would like to thank all the students of the Computer Science Department for support over the
years, and especially the members of the Graphics Group. Most of my years as an undergraduate here
have been spent in Blacker Hovse, and I thank each of its inhabitants for making it such a great place
to live in.
I thank my sister, Roopa, for her advice and unflagging encouragement; and my parents for their
unwavering support.
description: Submitted - TR000549.pdf