Towards a Normalised 3d Geovisualisation: The Viewpoint Management

Abstract

ABSTRACT:This paper deals with the viewpoint management in 3D environments considering an allocentric environment. The recent advances in computer sciences and the growing number of affordable remote sensors lead to impressive improvements in the 3D visualisation. Despite some research relating to the analysis of visual variables used in 3D environments, we notice that it lacks a real standardisation of 3D representation rules. In this paper we study the "viewpoint" as being the first considered parameter for a normalised visualisation of 3D data. Unlike in a 2D environment, the viewing direction is not only fixed in a top down direction in 3D. A non-optimal camera location means a poor 3D representation in terms of relayed information. Based on this statement we propose a model based on the analysis of the computational display pixels that determines a viewpoint maximising the relayed information according to one kind of query. We developed an OpenGL prototype working on screen pixels that allows to determine the optimal camera location based on a screen pixels colour algorithm. The viewpoint management constitutes a first step towards a normalised 3D geovisualisation.

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

“…Based on the algorithm developed by [105], we manage the 3D viewpoint so as to determine an optimal position and orientation of the camera for the visualisation of three kinds of tesserae distinguished by their material: faience, gold and silver. 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 facilitate the dissemination of information, query results from specific queries need to be visualized properly. For users to access and share a common viewpoint result of a semantic query, we enhanced the approach in [105] by applying over each object (i.e., tessera) one unique colour per instance for each class (e.g., faience pieces); all non-requested tesserae are coloured in black as in Figure 9. Finally, semantic information is transferred to the point cloud that can be used for information extraction.…”

“…The bounding box centre becomes the centre of the sphere on which the camera will move to determine the optimal camera position. The coordinates of the camera on the sphere are computed according to the following formulae [105]: Figure 9. …”

Digital Investigations of an Archaeological Smart Point Cloud: A Real Time Web-Based Platform to Manage the Visualisation of Semantical Queries

“…Based on the algorithm developed by (Neuville et al, 2016) we manage the 3D viewpoint so as to determine an optimal position and orientation of the camera for the visualisation of three kinds of tesserae distinguished by their material: faience, gold and silver (Table 1). Through the previous steps of recognition and semantization described in 3.2, we are now able to exploit the semantically rich point cloud data structure in order to visualise efficiently the different sorts of tesserae.…”

“…To achieve it, we use the method developed by (Neuville et al, 2016) based on the analysis of the computational display pixels. It determines an optimal viewpoint by maximising the visibility of objects selected by semantic queries.…”