An Automatic Digital Terrain Generation Technique for Terrestrial Sensing and Virtual Reality Applications
This addresses the challenge of handling massive point cloud data in terrestrial remote sensing and VR applications, though it appears incremental as it builds on existing terrain modeling methods.
The paper tackles the problem of manually modeling terrain from large point cloud data by introducing an automatic technique that generates accurate and physically realistic Digital Terrain Models efficiently, with real-time performance for large landforms and compatibility with Virtual Reality environments.
The identification and modeling of the terrain from point cloud data is an important component of Terrestrial Remote Sensing (TRS) applications. The main focus in terrain modeling is capturing details of complex geological features of landforms. Traditional terrain modeling approaches rely on the user to exert control over terrain features. However, relying on the user input to manually develop the digital terrain becomes intractable when considering the amount of data generated by new remote sensing systems capable of producing massive aerial and ground-based point clouds from scanned environments. This article provides a novel terrain modeling technique capable of automatically generating accurate and physically realistic Digital Terrain Models (DTM) from a variety of point cloud data. The proposed method runs efficiently on large-scale point cloud data with real-time performance over large segments of terrestrial landforms. Moreover, generated digital models are designed to effectively render within a Virtual Reality (VR) environment in real time. The paper concludes with an in-depth discussion of possible research directions and outstanding technical and scientific challenges to improve the proposed approach.