Abstract
With the development of 3D measurement systems, dense colored point clouds are increasingly available. However, up to now, their use in interactive applications has been restricted by the lack of support for point clouds in game engines. In addition, many of the existing applications for point clouds lack the capacity for fluent user interaction and application development. In this paper, we present the development and architecture of a game engine extension facilitating the interactive visualization of dense point clouds. The extension allows the development of game engine applications where users edit and interact with point clouds. To demonstrate the capabilities of the developed extension, a virtual reality head-mounted display is used and the rendering performance is evaluated. The result shows that the developed tools are sufficient for supporting real-time 3D visualization and interaction. Several promising use cases can be envisioned, including both the use of point clouds as 3D assets in interactive applications and leveraging the game engine point clouds in geomatics.
Highlights
Methods for producing dense, colored point clouds are increasingly available
The system facilitates first the preparing of the point cloud for game engine use in the Unity editor, secondly for visualizing it in the editor, and for using Unity to build stand-alone applications that utilize the extension for efficient visualization of the prepared point clouds (Fig. 7)
This may be performed either in the editor or in a stand-alone application produced with Unity
Summary
Methods for producing dense, colored point clouds are increasingly available. Three-dimensional measuring and reconstruction can be achieved with image-based techniques (e.g., Toschi et al, 2017; Micheletti et al, 2015), laser scanning, or a number of other techniques (e.g., structured light systems). 3D measuring methods have improved both in efficiency (e.g., Nocerino et al, 2017; Kukko et al, 2017; Li et al, 2019) and consumer availability (e.g. Diakité and Zlatanova, 2016; Hyyppä et al, 2017; Zollhöfer et al, 2018). Dense point clouds have been identified as a significant data type by several researchers (Virtanen et al, 2017; Cura et al, 2017; Poux et al, 2016; Otepka et al, 2013). This has led to the development of management systems for massive point cloud data sets (van Oosterom et al, 2015; Cura et al, 2017; El-Mahgary et al, 2020), and point cloud visualization (Deibe et al, 2019)
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