Modeling and Enhancement of LiDAR Point Clouds from Natural Scenarios
Published in Eurographics - Posters, 2022
Abstract
The generation of realistic natural scenarios is a longstanding and ongoing challenge in Computer Graphics. A common source of real-environmental scenarios is open point cloud datasets acquired by LiDAR (Laser Imaging Detection and Ranging) devices. However, these data have low density and are not able to provide sufficiently detailed environments. In this study, we propose a method to reconstruct real-world environments based on data acquired from LiDAR devices that overcome this limitation and generate rich environments, including ground and high vegetation. Additionally, our proposal segments the original data to distinguish among different kinds of trees. The results show that the method is capable of generating realistic environments with the chosen density and including specimens of each of the identified tree types.
Recommended citation: Collado Araque, J. A., López Ruiz, A., Jiménez, J. R., Ortega, L. M., Feito, F., & Jurado, J. M. (2022). Modeling and Enhancement of LiDAR Point Clouds from Natural Scenarios. In Eurographics 2022 - Posters (pp. 35–36). The Eurographics Association. https://doi.org/10.2312/EGP.20221016.
Download Paper | Download Bibtex