Innovative Methodology of On-Line Point Cloud Data Compression for Free-Form Surface Scanning Measurement

Yan Li,Ye Tao,Zhengmeng Hou,Yuyong Ma

doi:10.3390/app8122556

Abstract

In order to obtain a highly accurate profile of a measured three-dimensional (3D) free-form surface, a scanning measuring device has to produce extremely dense point cloud data with a great sampling rate. Bottlenecks are created owing to inefficiencies in manipulating, storing and transferring these data, and parametric modelling from them is quite time-consuming work. In order to effectively compress the dense point cloud data obtained from a 3D free-form surface during the real-time scanning measuring process, this paper presents an innovative methodology of an on-line point cloud data compression algorithm for 3D free-form surface scanning measurement. It has the ability to identify and eliminate data redundancy caused by geometric feature similarity between adjacent scanning layers. At first, the new algorithm adopts the bi-Akima method to compress the initial point cloud data; next, the data redundancy existing in the compressed point cloud is further identified and eliminated; then, we can get the final compressed point cloud data. Finally, the experiment is conducted, and the results demonstrate that the proposed algorithm is capable of obtaining high-quality data compression results with higher data compression ratios than other existing on-line point cloud data compression/reduction methods.

Highlights

With the rapid development of modern industry, three-dimensional (3D) free-form surface parts are being utilized more and more widely
The on-line point cloud data compression algorithm was tested in the industrial real-time measuring process and compared with existing methods
In an attempt to effectively compress dense point cloud data obtained from a 3D free-form surface during the real-time scanning measuring process, this paper presents a novel on-line point cloud data compression algorithm which has the ability to identify and eliminate data redundancy caused by geometric feature similarity between adjacent scanning layers

Summary

Introduction

With the rapid development of modern industry, three-dimensional (3D) free-form surface parts are being utilized more and more widely. These involve, but are not limited to, aviation, aerospace, shipbuilding, automotive, biomedical and home appliance industries [1,2]. The automated physical models with free-form surfaces [14,15,16,17]. In order to obtain a high-quality profile of a measured surface, scanning measuring devices have to produce massive amounts of point cloud data with great sampling rates, and not all these points are indispensable [18,19,20].

Methods

Results

Discussion

Conclusion