A Survey on Processing of Large-Scale 3D Point Cloud
This paper provides a comprehensive overview of the state-of-the-art for processing large-scale 3D point cloud based on optical acquisition. We first summarize the general pipeline of point cloud processing, ranging from filtering to the final reconstruct
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Abstract. This paper provides a comprehensive overview of the state-of-the-art for processing large-scale 3D point cloud based on optical acquisition. We first summarize the general pipeline of point cloud processing, ranging from filtering to the final reconstruction, and give further detailed introduction. On this basis we give a general insight over the previous and latest methods applying LIDAR and remote sensing techniques as well as Kinect on analysis techniques, including urban environment and cluttered indoor scene. We also focus on the various approaches of 3D laser scenes scanning. The goal of the paper is to provide a comprehensive understanding on the point cloud reconstruction methods based on 3D laser scanning techniques, and make forecasts for future research issues. Keywords: Lidar Point cloud Reconstruction Urban environment Indoor scene
1 Introduction Large-scale 3D point cloud and LIDAR (Light Detection And Ranging) technique are hot topics that gradually emerges and become ubiquitous in recent years, mainly used for large-scale 3D point cloud generation. Currently acquisition of both indoor and outdoor environments is widely developed and used in many fields such as navigation, architecture and real estate, and is getting popularity thanks to the appearance of 3D laser scanning machines and range cameras. Compared to other modeling techniques, the merits of point cloud data obtained by LIDAR and Kinect are irreplaceable. First, the data is real and truthful, like the saying “what you see is what you get”. Second, big scale data indicates millions of points or even more, which contains rich information to be processed such as millimeter level accuracy. However, the existing noise makes it difficult to calculate interlaced objects like trees or other plants. Another shortage in current methods is the lack of combination of position, color and strength together to generate models. The existing algorithms usually deal with point cloud position but ignore true color of each point, which needs further improvement. To achieve better results from the large-scale scanning point cloud data by LIDAR, many studies have attempted to establish or improve the point cloud processing algorithms. In these methods, the major challenge lies in how to identify the noise and © Springer International Publishing Switzerland 2016 A. El Rhalibi et al. (Eds.): Edutainment 2016, LNCS 9654, pp. 267–279, 2016. DOI: 10.1007/978-3-319-40259-8_24
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classify the cluttered scene. Fortunately, there are some open source libraries emerged for dealing with point cloud, i.e., Point Cloud Library (PCL) of [1], which is a fully developed library for n-D Point Clouds and 3D geometry processing.
2 Point Cloud Processing The processing of point cloud has already been developed and regulated as sophisticated mechanisms. We summarize the basic steps for the point cloud processing as shown in Fig. 1.
Filtering
Feature Computation
Data Structure
Surface Generation
Sample Consensus
Keypoint Extraction
Registrati
Data Loading...
