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Remote Sens. 2014, 6(8), 7212-7232; doi:10.3390/rs6087212

Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures

1
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
2
Chinese Academy of Surveying and Mapping, Beijing 100830, China
*
Authors to whom correspondence should be addressed.
Received: 22 May 2014 / Revised: 3 July 2014 / Accepted: 28 July 2014 / Published: 4 August 2014
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Abstract

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. View Full-Text
Keywords: airborne LiDAR; multi-core computing; stream computing; progressive TIN densification; filtering airborne LiDAR; multi-core computing; stream computing; progressive TIN densification; filtering
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Kang, X.; Liu, J.; Lin, X. Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures. Remote Sens. 2014, 6, 7212-7232.

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