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This paper introduces a method for automatically transforming a point cloud from a laser scanner into a volumetric 3D building model based on the new concept of enclosure reasoning. Rather than simply classifying and modeling building surfaces independently or with pair wise contextual relationships, this work introduces room, floor and building level reasoning. Enclosure reasoning premises that rooms are cycles of walls enclosing free interior space. These cycles should be of minimum description length (MDL) and obey the statistical priors expected for rooms. Floors and buildings then contain the best coverage of the mostly likely rooms. This allows the pipeline to generate higher fidelity models by performing modeling and recognition jointly over the entire building at once. The complete pipeline takes raw, registered laser scan surveys of a single building. It extracts the most likely smooth architectural surfaces, locates the building, and generates wall hypotheses. The algorithm then optimizes the model by growing, merging, and pruning these hypotheses to generate the most likely rooms, floors, and building in the presence of significant clutter. |
