Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such ...visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset.
In airborne laser bathymetry knowledge of exact water level heights is a precondition for applying run-time and refraction correction of the raw laser beam travel path in the medium water. However, ...due to specular reflection especially at very smooth water surfaces often no echoes from the water surface itself are recorded (drop outs). In this paper, we first discuss the feasibility of reconstructing the water surface from redundant observations of the water bottom in theory. Furthermore, we provide a first practical approach for solving this problem, suitable for static and locally planar water surfaces. It minimizes the bottom surface deviations of point clouds from individual flight strips after refraction correction. Both theoretical estimations and practical results confirm the potential of the presented method to reconstruct water level heights in dm precision. Achieving good results requires enough morphological details in the scene and that the water bottom topography is captured from different directions.
We present an image-based approach to generate truly random numbers from the surface of water bodies such as oceanic bays. As a natural phenomenon, wind-generated gravity waves have non-deterministic ...behavior. We use the randomness of the angular relation between pairs of estimated surface normals to generate uniformly distributed random binary digits and build random numbers from those digits. Our approach produces compelling geometric models of water surfaces and generates random numbers with high entropy.
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