A Framework for Representing and Reasoning about Three-Dimensional Objects for Visione

Ellen Lowenfeld Walker, Takeo Kanade, Martin Herman

Abstract


The capabilities for representing and reasoning about three-dimensional (3-D) objects are essential for knowledge-based, 3-D photointerpretation systems that combine domain knowledge with image processing, as demonstrated by 3- D Mosaic and ACRONYM. Three-dimensional representation of objects is necessary for many additional applications, such as robot navigation and 3-D change detection. Geometric reasoning is especially important because geometric relationships between object parts are a rich source of domain knowledge. A practical framework for geometric representation and reasoning must incorporate projections between a two-dimensional (2-D) image and a 3-D scene, shape and surface properties of objects, and geometric and topological relationships between objects. In addition, it should allow easy modification and extension of the system's domain knowledge and be flexible enough to organize its reasoning efficiently to take advantage of the current available knowledge. We are developing such a framework -- the Frame-based Object Recognition and Modeling (3-D FORM) System. This system uses frames to represent objects such as buildings and walls, geometric features such as lines and planes, and geometric relationships such as parallel lines. Active procedures attached to the frames dynamically compute values as needed. Because the order of processing is controlled largely by the order of slot access, the system performs both top-down and bottom-up reasoning, depending on the current available knowledge. The FORM system is being implemented with the Carnegie-Mellon University-built Framekit tool in Common Lisp (Carbonell and Joseph 1986). To date, it has been applied to two types of geometric reasoning problems: interpreting 3-D wire frame data and solving sets of geometric constraints.

Full Text:

PDF


DOI: http://dx.doi.org/10.1609/aimag.v9i2.675

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.