Integration of Point Clouds and Images Acquired From a Low-Cost Nir Camera Sensor for Cultural Heritage Purposes

Abstract

ABSTRACT:Terrestrial Laser Scanning is currently one of the most common techniques for modelling and documenting structures of cultural heritage. However, only geometric information on its own, without the addition of imagery data is insufficient when formulating a precise statement about the status of studies structure, for feature extraction or indicating the sites to be restored. Therefore, the Authors propose the integration of spatial data from terrestrial laser scanning with imaging data from low-cost cameras. The use of images from low-cost cameras makes it possible to limit the costs needed to complete such a study, and thus, increasing the possibility of intensifying the frequency of photographing and monitoring of the given structure. As a result, the analysed cultural heritage structures can be monitored more closely and in more detail, meaning that the technical documentation concerning this structure is also more precise. To supplement the laser scanning information, the Authors propose using both images taken both in the near-infrared range and in the visible range. This choice is motivated by the fact that not all important features of historical structures are always visible RGB, but they can be identified in NIR imagery, which, with the additional merging with a threedimensional point cloud, gives full spatial information about the cultural heritage structure in question. The Authors proposed an algorithm that automates the process of integrating NIR images with a point cloud using parameters, which had been calculated during the transformation of RGB images. A number of conditions affecting the accuracy of the texturing had been studies, in particular, the impact of the geometry of the distribution of adjustment points and their amount on the accuracy of the integration process, the correlation between the intensity value and the error on specific points using images in different ranges of the electromagnetic spectrum and the selection of the optimal method of transforming the acquired imagery. As a result of the research, an innovative solution was achieved, giving high accuracy results and taking into account a number of factors important in the creation of the documentation of historical structures. In addition, thanks to the designed algorithm, the final result can be obtained in a very short time at a high level of automation, in relation to similar types of studies, meaning that it would be possible to obtain a significant data set for further analyses and more detailed monitoring of the state of the historical structures.

Accurate Reconstruction of the LoD3 Building Model by Integrating Multi-Source Point Clouds and Oblique Remote Sensing Imagery

“…The integration of point clouds and imagery data for detailed building modelling can help to solve the above problems [14,[25][26][27][28][29]. Researchers have made some trials in the integration of multi-source data for reconstructing the building models.…”

“…Kedzierski and Fryskowska [30] discussed and analysed different methods for the integration methods of terrestrial and airborne laser scanning data, and evaluated their accuracies for building model reconstruction. Later, Kedzierski et al [29] proposed an automated algorithm to integrate the terrestrial point clouds and remote sensing images in the near-infrared and visible range, which achieves good accuracy results for cultural heritage documentation. Pu [25] extracted feature lines from single-view close range images to refine the edges of building models.…”

Enhancing Image-Based Multiscale Heritage Recording with Near-Infrared Data

“…Past approaches regarding the integration of metric heritage modeling and information in the near-infrared spectrum have primarily concentrated on separate data acquisitions. Some methods explored for the production of models enhanced with NIR texture through two-dimensional (2D)-to-3D registration are (1) mathematical transformation of the spectral images using corresponding points [25], (2) mutual information methods, as with utilization of depth maps [26] or silhouette maps, reflection maps, and other illumination-based renderings [27], and (3) registration based on known sensor position, as can be performed with optical tracking of the cameras implemented for spectral acquisition [28]. However, the implementation of these approaches is often expensive due to the multiple sensors used and time-consuming due to the frequent need to develop application-specific algorithms.…”