点击次数：

论文类型：期刊论文

发表时间：2019-08-01

发表刊物：COMPUTER-AIDED DESIGN

收录刊物：SCIE、EI

卷号：113

页面范围：48-61

ISSN号：0010-4485

关键字：Sculptured surface; Reasoning mechanism; Data reduction; Point cloud; Selective sampling

摘要：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 online point cloud data reduction/selective sampling methods. Experiment is conducted and the results demonstrate the superior performance of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.