Building Reconstruction Based on Mdl Principle from 3-d Feature Points

For 3-D building reconstructions of urban areas, we present a fully automatic shape recovery method that uses 3-D points acquired from aerial image sequences. This paper focuses on shape recovery of flat rooftops that are parallel to the ground. We recover each rooftop from a set of 3-D points located at nearly the same height. Such 3-D point sets are made by merging point sets under the MDL (Minimum Description Length) principle, which finds suitably concise point sets for 3-D building models. Often, only parts of rooftop shapes can be recovered because of the 3-D position errors being generated in the points. To refine the recovered shapes, we merge the parts under a heuristic condition in which shapes will have a pair of orthogonally oriented edges. To optimize parameters and estimate the viability of our method, we defined a success rate, called the cover ratio, as the area in which the recovered shape and a correct shape (given as reference data) overlap to the combined area of the recovered and correct shapes. Experimental results showed that our method achieved a cover ratio of 75.25%, and through improved cover ratio we also confirmed effectiveness of shape refinement. We also found that even if only one-ninth of the reference data could be used in the optimization of parameters, the cover ratio was 70.96%. The experimental results we obtained showed that our point-based method was effective in enabling the recovery of buildings in urban areas.