Abstract

In general, multiple views are required to create a complete 3-D model of an object or a multiroomed indoor scene. In this work, we address the problem of merging multiple textured 3-D data sets, each of which corresponding to a different view of a scene or object. There are two steps to the merging process: registration and integration. Registration is the process by which data sets are brought into alignment. To this end, we use a modified version of the Iterative Closest Point algorithm (ICP); our version, which we call color ICP, considers not only 3-D information, but color as well. This has shown to have resulted in improved performance. Once the 3-D data sets have been registered, we then integrate them to produce a seamless, composite 3-D textured model. Our approach to integration uses a 3-D occupancy grid to represent likelihood of spatial occupancy through voting. The occupancy grid representation allows the incorporation of sensor modeling. The surface of the merged model i...

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