Mostrando entradas con la etiqueta 3D reconstruction. Mostrar todas las entradas
Mostrando entradas con la etiqueta 3D reconstruction. Mostrar todas las entradas

domingo, 6 de diciembre de 2020

Developing a strategy for precise 3D modelling of large-scale scenes for VR



In this work, it is presented a methodology for precise 3D modelling and multi-source geospatial data blending for the purposes of Virtual Reality immersive and interactive experiences. It has been evaluated on the volcanic island of Santorini due to its formidable geological terrain and the interest it poses for scientific and touristic purposes.

The methodology developed here consists of three main steps: Initially, bathymetric and SRTM (Shuttle Radar Topography Mission) data are scaled down to match the smallest resolution of the datasetAfterwards, the resulted elevations are combined based on the slope of the relief, while considering a buffer area to enforce a smoother terrain. As a final step, the orthophotos are combined with the estimated DTM (Digital Terrain Model) via applying a nearest neighbour matching schema leading to the final terrain background.

In addition to this, both onshore and offshore points-of-interest were modelled via image-based 3D reconstruction and added to the virtual scene. The overall geospatial data that need to be visualized in applications demanding phototextured hyper-realistic models pose a significant challenge. The 3D models are treated via a mesh optimization workflow, suitable for efficient and fast visualization in virtual reality engines, through mesh simplification, physically based rendering texture maps baking, and level-of-details. 

Read more at https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2020/567/2020/isprs-archives-XLIII-B4-2020-567-2020.pdf

domingo, 8 de noviembre de 2020

3D Fire Front Reconstruction in UAV-Based Forest-Fire Monitoring System



This work presents a new method of 3D reconstruction of the forest-fire front based on uncertain observations captured by remote sensing from UAVs within the forest-fire monitoring system.

The use of multiple cameras simultaneously to capture the scene and recognize its geometry including depth is proposed. Multi-directional observation allows perceiving and representing a volumetric nature of the fire front as well as the dynamics of the fire process.

The novelty of the proposed approach lies in the use of soft rough set to represent forest fire model within the discretized hierarchical model of the terrain and the use of 3D CNN (3D Convolutional Neural Network) to classify voxels within the reconstructed scene.

The developed method provides sufficient performance and good visual representation to fulfill the requirements of fire response decision makers. 

Read more at: https://ieeexplore.ieee.org/abstract/document/9204196