One billion points in the cloud – an octree for efficient processing of 3D laser scans |
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| Institution: | 1. Key Laboratory of Geographical Information Science, Ministry of Education, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China;2. Key Laboratory of Analysis and Processing on Big Data of Henan Province, Henan University, 85 Minglun Road, Kaifeng, Henan 475001, China;3. Department of Computer Science & Information Systems, College of Business University of North Alabama, Florence, Alabama, United States;4. Naval Academy Research Institute, Brest, France |
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| Abstract: | Automated 3-dimensional modeling pipelines include 3D scanning, registration, data abstraction, and visualization. All steps in such a pipeline require the processing of a massive amount of 3D data, due to the ability of current 3D scanners to sample environments with a high density. The increasing sampling rates make it easy to acquire Billions of spatial data points. This paper presents algorithms and data structures for handling these data. We propose an efficient octree to store and compress 3D data without loss of precision. We demonstrate its usage for an exchange file format, fast point cloud visualization, sped-up 3D scan matching, and shape detection algorithms. We evaluate our approach using typical terrestrial laser scans. |
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