SURFACE RECONSTRUCTION BY SMOOTHNESS MAP ESTIMATION

摘要

This paper presents a new method for reconstructing object surface from sparse data while preserving discontinuities. The proposed method is an extention of regularization by applying smoothness map as an array of regularization parameters. Existing methods that preserve discontinuity simply change the array of regularization parameters based on local features. Therefore, the results using these methods are affected by not only local feature but also noise. This paper proposes a method to obtain the smoothness map by taking the global structure of the shape into consideration. To evaluate the global structure, the smoothness map should be described as simply as possible. We apply the minimum description length (MDL) principle to obtain an appropriate smoothness map as it describes the best surface model. Then, we show that the smoothness map can be estimated by minimizing the description length for the smoothness map while fitting the surface to the observed data. As a result, the proposed method can reconstruct object surfaces while preserving discontinuities using the estimated smoothness map. Experiments performed on real scenes are shown.