Reasoning mechanism: An effective data reduction algorithm for on-line point cloud selective sampling of sculptured surfaces

Abstract

For obtaining a high-quality profile of measured sculptured surface, scanning devices have to produce massive point cloud data with great sampling rates. Bottlenecks are created owing to inefficiencies in storing, manipulating and transferring these data, and the parametric modeling from them is quite a time-consuming work. The purpose of this paper is to effectively simplify point cloud data from a measured sculptured surface during the on-line point cloud data selective sampling process. The key contribution is the generation of a novel reasoning mechanism which is based on a predictor–corrector scheme, and it is capable of eliminating data redundancy caused by spatial similarity of collected point clouds. In particular, this mechanism is embedded in our newly designed framework for on-line point cloud data selective sampling of sculptured surfaces. This framework consists of two stages: First, the initial point data flow is selective sampled using bi-Akima method; second, the data flow is refined based on our proposed reasoning mechanism. Moreover, our versatile framework is capable of obtaining high-quality resampling results with smaller data reduction ratio than other existing on-line point cloud data reduction/selective sampling methods. Experiment is conducted and the results demonstrate the superior performance of the proposed method.