画像特徴量を用いた大規模点群データからの円柱面と矩形面の検出(機械要素,潤滑,設計,生産加工,生産システムなど)

Abstract

The state-of-the-art mid/long-range laser scanners can capture dense point-clouds from engineering facilities. However, it is very tedious work to manually segment large-scale point-clouds and fit surfaces to the subsets of point-clouds for constructing 3D models. In this paper, we propose a new method to automatically extract cylindrical faces and rectangle faces, which are commonly used for standard parts in engineering facilities. Since it requires a lot of time and memory to search surfaces in large-scale point-clouds, we generate distance images and intensity images from dense point-clouds and estimate canditates of cylindrical and rectangle regions using image processing techniques. Then, we calculate equations of surfaces by applying the region-growing method to the candidate regions, and detect rectangle and cylindrical faces