太阳成8722(中国)有限公司-GREEN NO.1

标题：Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures作者：Kang, Xiaochen; Liu, Jiping; Lin, Xiangguo

来源出版物：REMOTE SENSING 卷：6 期：8 页：7212-7232 DOI：10.3390/rs6087212 出版年：AUG 2014

摘要：As one of the key steps in the processing of airborne light detection and ranging (LiDAR) data, filtering often consumes a huge amount of time and physical memory. Conventional sequential algorithms are often inefficient in filtering massive point clouds, due to their huge computational cost and Input/Output (I/O) bottlenecks. The progressive TIN (Triangulated Irregular Network) densification (PTD) filter is a commonly employed iterative method that mainly consists of the TIN generation and the judging functions. However, better quality from the progressive process comes at the cost of increasing computing time. Fortunately, it is possible to take advantage of state-of-the-art multi-core computing facilities to speed up this computationally intensive task. A streaming framework for filtering point clouds by encapsulating the PTD filter into independent computing units is proposed in this paper. Through overlapping multiple computing units and the I/O events, the efficiency of the proposed method is improved greatly. More importantly, this framework is adaptive to many filters. Experiments suggest that the proposed streaming PTD (SPTD) is able to improve the performance of massive point clouds processing and alleviate the I/O bottlenecks. The experiments also demonstrate that this SPTD allows the quick processing of massive point clouds with better adaptability. In a 12-core environment, the SPTD gains a speedup of 7.0 for filtering 249 million points.

入藏号：WOS:000341518700019

文献类型：Article

语种：English

作者关键词：airborne LiDAR, multi-core computing, stream computing, progressive TIN densification, filtering

扩展关键词：URBAN AREAS; SEGMENTATION; CLASSIFICATION; ALGORITHMS; EXTRACTION

通讯作者地址：[Kang, Xiaochen; Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址：kxc2005@126.com; liujp@casm.ac.cn; linxiangguo@gmail.com

地址：

[Kang, Xiaochen] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Liu, Jiping; Lin, Xiangguo] Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China.

研究方向：Remote Sensing

ISSN：2072-4292