Automatic Detection and Classi cation of Objects in Point Clouds using multi-stage Semantics

Due to the increasing availability of large unstructured point clouds from lasers scanning and photogrammetry, there is a growing demand for automatic evaluation methods. Given the complexity of the underlying problems, several new methods resort to using semantic knowledge in particular for object detection and classification support. In this paper, we present a novel approach, which makes use of advanced algorithms, and benefits from intelligent knowledge management strategies for the processing of 3D point clouds and object classification in a scanned scene. In particular, our method extends the use of semantic knowledge to all stages of the processing, including the guidance of the 3D processing algorithms. The complete solution consists of a multi-stage, iterative, concept based on three factors: the modeled knowledge, the package of algorithms, and the classification engine.

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