Abstract
Abstract. The rapid development of 3D scanning technology is a welcome progress in the field of tangible cultural heritage documentation. While active sensors such as handheld Time-of-Flight (ToF) cameras and lidar have recently generated much hype, developments in low-cost imaging sensors have also seen long strides in recent decades. This paper aims to see the potential of videogrammetry for the purposes of heritage documentation. This technique has existed for decades, but we argue that when combined with modern smartphone sensors and proper photogrammetric processing workflow it may present an interesting low-cost solution for 3D scanning. Furthermore, the paper wishes to address the requirement for a certain geometric quality in heritage documentation and how the proposed method may fulfil them. For this reason, comparisons between the videogrammetric result and traditional DSLR close range photogrammetry will be described to determine its suitability for heritage documentation. Results show that using modern low-cost smartphone imaging sensors, a good compromise between geometric quality and overall cost in the context of cultural heritage recording is possible to achieve.
Highlights
Videogrammetry or video-based photogrammetry has existed for a long time
The technique benefits greatly from developments in digital video sensors and image orientation algorithms (e.g. SLAM), it remains in the shadows of traditional image-based photogrammetry due to the inherent loss of resolution when passing from image to video
We argue that state-of-the-art smartphone videos may be used as a viable alternative for heritage documentation, for applications not requiring a high level of precision e.g. visualisation, VR, or AR
Summary
Videogrammetry or video-based photogrammetry has existed for a long time. The idea of extracting video frames to be used as input to photogrammetry was developed together with the advent of low-cost CCD sensors (Gruen, 1997). The technique benefits greatly from developments in digital video sensors and image orientation algorithms (e.g. SLAM), it remains in the shadows of traditional image-based photogrammetry due to the inherent loss of resolution when passing from image to video. The technique presents obvious advantages vis-à-vis classical photogrammetry. Acquisition time is greatly reduced because the user only needs to take a video shot compared to the more time-consuming multi-image acquisition. The quality of the resulting 3D data is often severely limited by the quality of the video and the frame sequencing itself (Flies et al, 2019)
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