L2-3650

Period: 01. 05. 2010 – 01. 05. 2013

Funding SOURCE: ARIS

REFERENCE NR.: L2-3650

PROJECT PROGRAMME: applied research project

COORDINATORS AND CONTACTS: prof. dr. Borut Žalik

Project website: gemma.feri.um.si/lidar/

LINKEDIN: /

Processing of massive geometric LIDAR data

Abstract:

Acquisition and processing of geometric data from the Earth surface are complex processes that used to be considered difficult, slow and expensive. However, modern technologies enabled development of devices capable of fast and data capturing. The focus has therefore been moved to data storage and processing. LiDAR (Light Detection and Ranging) scanners can capture up to 24 points in a square metre, with the acquisition speed as high as 200.000 points per second. The results of this are huge amounts of geometric data – 3D points with attached specific data that exceed the storage capacities of a computer system. Their storage and processing require special approaches. The development of software for huge datasets processing hardly follows capabilities of data capture devices. This implies many challenges and problems in the field of geometric data processing. In the proposed project, we deal with some of them in close cooperation with final users. These problems are represented in greater detail in continuation. The companies Igea d.o.o.X-Lab d.o.o.Dat-Con d.o.o. and Geoin d.o.o., which partially fund the proposed project, are either data providers or distributors. The expected solutions will directly support the quality of their services. To achieve the set goals, thorough theoretical knowledge of the field, new theoretical solutions and practical experiences are necessary. Proven excellence of our previous research results and successful transfers to practice provide a good starting point.

The project activities will run in three main directions, where the particular solutions will supplement each other. Visualization of geometric data represents the most natural way for results adequacy evaluation. Because of huge amounts of data, a LOD-based (level of details) approach will be used. Since we deal with unstructured data, we will focus on point-based rendering (PBR) method and graphic processors utilization. Existing PBR visualization of LiDAR data still does not reach the satisfactory quality and, therefore, improvements are extremely important from theoretical and also practical point of view.

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