| Hi-index | 0.00 |
Planar surface patches, extracted for instance from a captured 3D point cloud, play an important role as a feature in modeling and/or recognizing objects and environments. In terms of extracting such planar surface patches, the main technical issue involved may be of overcoming the trade-off between the modeling accuracy and the computational efficiency, especially, when the 3D point cloud captured is noisy. Conventionally, they have taken approaches that seek either for high computational efficiency at the expense of modeling accuracy or vice versa. This paper contributes the advancement in the methodology of extracting planar surface patches from a noisy 3D point cloud by presenting a method that provides high modeling accuracy yet under low computational cost. The major contribution of the proposed method is summarized as follows: 1) A robust estimation of surface normal vectors in such a way as to minimize the effect of noise in the data. 2) An accurate localization of density peaks based on a spherical coordinate operator with a flexible size of sliding window used for estimating the density of surface normal vectors represented on a unit spherical surface as a means of accurately identifying those planar surface patches of the same orientation. 3) A segmentation of individual planar surface patches of the same orientation by projecting those 3D points clustered as belonging to a same peak onto x, y and z axes of the Cartesian coordinate with the Y axis representing their orientation. The experimental results show the effectiveness of the proposed method in comparison with conventional ones for various indoor and outdoor images.