3D Simplification Methods and Large Scale Terrain Tiling

Abstract

This paper tackles the problem of generating world-scale multi-resolution triangulated irregular networks optimized for web-based visualization. Starting with a large-scale high-resolution regularly gridded terrain, we create a pyramid of triangulated irregular networks representing distinct levels of detail, where each level of detail is composed of small tiles of a fixed size. The main contribution of this paper is to redefine three different state-of-the-art 3D simplification methods to efficiently work at the tile level, thus rendering the process highly parallelizable. These modifications focus on the restriction of maintaining the vertices on the border edges of a tile that is coincident with its neighbors, at the same level of detail. We define these restrictions on the three different types of simplification algorithms (greedy insertion, edge-collapse simplification, and point set simplification); each of which imposes different assumptions on the input data. We implement at least one representative method of each type and compare both qualitatively and quantitatively on a large-scale dataset covering the European area at a resolution of 1/16 of an arc minute in the context of the European Marine Observations Data network (EMODnet) Bathymetry project. The results show that, although the simplification method designed for elevation data attains the best results in terms of mean error with respect to the original terrain, the other, more generic state-of-the-art 3D simplification techniques create a comparable error while providing different complexities for the triangle meshes.

Scientific Visualization on the Cloud: the NEANIAS Services towards EOSC Integration

“…The data context is underpinned by the rapid growth of underwater mapping sensors which enables retrieving the shape of the Earth's seafloor at unprecedented levels of fidelity. Computationallyefficient visualization of such datasets is needed for underwater communities, by generating world-scale multi-resolution triangulated irregular networks optimized for web-based visualization [6].…”

“…The EOSC roadmap 5 foresees the seamless federation of existing and future research data infrastructures, as well as other large-scale scientific networks and initiatives. Its vision envisages the adoption of a common policy framework to enable FAIR 6 (i.e. Findable, Accessible, Interoperable and Reusable) data to be used and shared throughout the entire value chain for scientific, societal and industrial purposes.…”

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

“…Since the USV is also designed to host an onboard computer and disposes of a larger power supply, more expensive computations will be performed on the USV. Algorithms for MBES data analysis can return a simplified bathymetric representation [77] as well as a preliminary characterization of the terrain attributes useful for habitat-mapping tasks [59]. Algorithms for image analysis can detect end classify relevant organisms contained in the images collected by the imaging device [10,72,78,79].…”