Abstract
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.
Highlights
In recent years, aerial data acquisition with LiDAR scanning systems has been used in such diverse scenarios as digital elevation model acquisition [1,2], discovery/reconstruction of archaeological remains [3,4], estimating the vegetation density and/or height [5], etc
The second row (Figure 14c,d) displays augmentations together with intensity values, and the third row (Figure 14e,f) displays the final output of the proposed augmentation pipeline, where all of the points are merged, the colour information is added from orthophoto images and normal information is calculated using principal component analysis on the point cloud data
We presented a LiDAR point-cloud processing pipeline
Summary
Aerial data acquisition with LiDAR scanning systems has been used in such diverse scenarios as digital elevation model acquisition [1,2], discovery/reconstruction of archaeological remains [3,4], estimating the vegetation density and/or height [5], etc. While in most scenarios the gathered LiDAR data are used for analysis and digital terrain model development, it can be used for visualisation. This is especially true for large country-wide LiDAR datasets, which can be augmented with colour information from aerial orthophoto data—https://potree.entwine.io. //plas.io or as stand-alone applications (e.g., Cloud Compare—https://www.cloudcompare.org and MeshLab—http://www.meshlab.net), but the LiDAR data are rarely used for direct visualisations due to the many inconsistencies and missing parts which makes them less appealing.
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