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Point Cloud researchSMART POINT CLOUD

Bringing intelligence to point clouds

 

Point Cloud Modelling


3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture | 2018 | Remote Sensing Journal

Point Cloud vizualisation

DIGITAL INVESTIGATIONS OF AN ARCHAEOLOGICAL SMART POINT CLOUD: A REAL TIME WEB-BASED PLATFORM TO MANAGE THE VISUALISATION OF SEMANTICAL QUERIES | 2017 | ISPRS Archives While virtual copies of the real world tend to be created faster than ever through point clouds and derivatives, their working proficiency by all professionals’ demands adapted tools to facilitate knowledge dissemination. Digital investigations are changing the way cultural heritage researchers, archaeologists, and curators work and collaborate to progressively aggregate expertise through one common platform. In this paper, we present a web application in a WebGL framework accessible on any HTML5-compatible browser. It allows real time point cloud exploration of the mosaics in the Oratory of Germigny-des-Prés, and emphasises the ease of use as well as performances. Our reasoning engine is constructed over a semantically rich point cloud data structure, where metadata has been injected a priori. We developed a tool that directly allows semantic extraction and visualisation of pertinent information for the end users. It leads to efficient communication between actors by proposing optimal 3D viewpoints as a basis on which interactions can grow.

Point Cloud data fusion

POINT CLOUDS AS AN EFFICIENT MULTISCALE LAYERED SPATIAL REPRESENTATION | 2016 | EUROGRAPHICS Workshop 3D point clouds describe urban shape at different scales, precisions and resolutions depending on the underlying sensors and acquisition methodology. These factors influence the quality of the data, as well as its representativity. In this paper, we propose a multi-scale workflow to obtain a better description of the captured environment through a multi-scale representative point cloud, presenting an unlimited depth and multisensory data fusion. Our method is shown over a ”smart point cloud” data structure and based on data fusion principles retaining higher description and precision on overlapping areas. The concept is illustrated through a use case on the castle of Jehay (Belgium), where aerial LiDAR data, terrestrial laser scanner point cloud and photogrammetrybased reconstruction are combined to obtain a multi-scale data structure.

SMART POINT CLOUD: DEFINITION AND REMAINING CHALLENGES | 2016 | ISPRS Annals 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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