Abstract
Abstract. 3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (which can generate point clouds based on photogrammetry) can provide denser sampling of 3D surface geometry. Unfortunately, terrestrial laser scanning sensors are expensive and trained persons are needed to use them for point cloud acquisition. A potential effective 3D modelling can be generated based on a low cost smartphone sensor. Herein, we show examples of using smartphone camera images to generate 3D models of urban structures. We compare a smartphone based 3D model of an example structure with a terrestrial laser scanning point cloud of the structure. This comparison gives us opportunity to discuss the differences in terms of geometrical correctness, as well as the advantages, disadvantages and limitations in data acquisition and processing. We also discuss how smartphone based point clouds can help to solve further problems with 3D urban model generation in a practical way. We show that terrestrial laser scanning point clouds which do not have color information can be colored using smartphones. The experiments, discussions and scientific findings might be insightful for the future studies in fast, easy and low-cost 3D urban model generation field.
Highlights
Low-cost sensors are potentially an important source for automatic and instant generation of 3D models which can be useful for quick 3D urban model updating
We evaluate the reliability of point clouds generated automatically by multi-view photogrammetry applied on smartphone camera images
This mapping is mostly done using optical aerial or satellite images and texture mapping is applied onto 3D models of the scene
Summary
Low-cost sensors are potentially an important source for automatic and instant generation of 3D models which can be useful for quick 3D urban model updating. The quality of the models is questionable and the user may not have a good opinion about how to collect images for the best 3D modelling results. We evaluate the reliability of point clouds generated automatically by multi-view photogrammetry applied on smartphone camera images. We show how to align uncalibrated smartphone based point clouds with laser scanning point clouds for comparison. We discuss further applications where smartphone based point clouds can be useful in terms of time, budget and man effort efficiency
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