Smart Point Cloud: Definition and Remaining Challenges

Abstract

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the <i>smart point cloud</i>. 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.

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

“…Through the previous steps of recognition and semantization described, we are now able to exploit the semantically rich point cloud data structure [11] to visualise efficiently the different sorts of tesserae. To achieve this, we performed a pre-processing step, totally transparent to users, in which we compute the optimal camera positions on a 3D COLLADA model of the mosaic which is constituted of the minimum convex hull of each tesserae information stored in the database.…”

“…Therefore, a strong need for ways to integrate knowledge to point clouds is essential. This "intelligence spring" is categorized regarding 3 sources as identified in [11], being device knowledge (i.e., about tools and sensors), analytic knowledge (i.e., about algorithms, analysis and their results) and domain knowledge (i.e., about a specific field of application). Their rapprochement to point clouds is, however, a bottleneck that arises early in the processing workflow.…”

“…Indeed, to avoid external influential sources that degrade the information, this step demands adapted techniques to minimize errors including noise, outliers and misalignment. Filtering the data strongly depends on device knowledge [11].…”

“…demands adapted techniques to minimize errors including noise, outliers and misalignment. Filtering the data strongly depends on device knowledge [11]. Several sets of data from various contexts were acquired to perform different tests.…”

“…However while promising structures and workflow provide partial solutions for knowledge injection into point clouds [11,12], the integration, the maturation state as well as the link between semantic and spatial information is rudimentary in archaeology. Concepts and tools that simplify this process are rare, which complicates the merging of different experts' perceptions around cultural heritage applications.…”