Smart Point Cloud

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data
structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active
remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with
the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction
for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to
rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection,
machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing
approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This
contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.

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