Browsing by Author "Bormann, Pascal"

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    Real-time Indexing of Point Cloud Data During LiDAR Capture
    (The Eurographics Association, 2022) Bormann, Pascal; Dorra, Tobias; Stahl, Bastian; Fellner, Dieter W.; Peter Vangorp; Martin J. Turner
    We introduce a software system that is capable of indexing point cloud data in real-time as it is being captured by a LiDAR (Light Detection and Ranging) sensor. Our system extends the popular MNO (modifiable nested octree) structure so that it can be built progressively without knowing the bounding box of the point cloud. Using a task-based parallel algorithm incoming points are continuously processed and distributed to the octree nodes using grid-based sampling. Different task priority functions enable prioritization of either high point throughput or low latency. We provide a reference implementation of this system and evaluate it using both a synthetic and a real-world test scenario. The synthetic test demonstrates good scalability up to 16 threads, with maximum point throughputs of up to 1.8 million points per second. These numbers are verified on a sensor system using a Velodyne VLP-16 LiDAR sensor, where our system is able to index all data produced by the scanner in real-time.
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    A System for Fast and Scalable Point Cloud Indexing Using Task Parallelism
    (The Eurographics Association, 2020) Bormann, Pascal; Krämer, Michel; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    We introduce a system for fast, scalable indexing of arbitrarily sized point clouds based on a task-parallel computation model. Points are sorted using Morton indices in order to efficiently distribute sets of related points onto multiple concurrent indexing tasks. To achieve a high degree of parallelism, a hybrid top-down, bottom-up processing strategy is used. Our system achieves a 2.3x to 9x speedup over existing point cloud indexing systems while retaining comparable visual quality of the resulting acceleration structures. It is also fully compatible with widely used data formats in the context of web-based point cloud visualization. We demonstrate the effectiveness of our system in two experiments, evaluating scalability and general performance while processing datasets of up to 52.5 billion points.