OPTIMAL INFORMATION EXTRACTION OF LASER SCANNING DATASET BY SCALE-ADAPTIVE REDUCTION

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Zang, Y.^1¹  Yang, B.^2² 

[1] School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, China^1;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China^2

关键词: Multi-scale;    Surface variation;    Radial basis function;    Just-Noticeable-Difference;    Degradation;   

DOI  :  10.5194/isprs-archives-XLII-3-2209-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

3D laser technology is widely used to collocate the surface information of object. For various applications, we need to extract a good perceptual quality point cloud from the scanned points. To solve the problem, most of existing methods extract important points based on a fixed scale. However, geometric features of 3D object come from various geometric scales. We propose a multi-scale construction method based on radial basis function. For each scale, important points are extracted from the point cloud based on their importance. We apply a perception metric Just-Noticeable-Difference to measure degradation of each geometric scale. Finally, scale-adaptive optimal information extraction is realized. Experiments are undertaken to evaluate the effective of the proposed method, suggesting a reliable solution for optimal information extraction of object.

【 授权许可】

CC BY