Abstract
Abstract. In recent years, a growing interest in the 3D digitisation of museum assets has been pushed by the evident advantages of digital copies in supporting and advancing the knowledge, preservation and promotion of historical artefacts. Realising photo-realistic and precise digital twins of medium and small-sized movable objects implies several operations, still hiring open research problems and hampering the complete automation and derivation of satisfactory results while limiting processing time. The work examines some recurrent issues and potential solutions, summing up several experiences of photogrammetric-based massive digitisation projects. In particular, the article presents some insights into three crucial aspects of the photogrammetric pipeline. The first experiments tackle the Depth of Field (DoF) problem, especially when digitising small artefacts with macro-lenses. On the processing side, two decisive and time-consuming tasks are instead investigated: background masking and point cloud editing, exploring and proposing automatic solutions for speeding up the reconstruction process.
Highlights
3D results (Table 1, Figures 2-3) show that, without masking unsharp areas produced by the Depth of Field (DoF) effect, the dense point cloud reconstructions achieved with lower Fnumbers are noisier, requiring a longer editing time for the following steps
It should be noted that, when handling massive digitisation, limited times for image acquisitions could influence the choice of the capturing parameters, as smaller apertures typically lead to longer acquisition processes
The tests on DoF effects (Section 3) when images are acquired with macro-lenses proved that adequate capture settings are crucial for delivering precise and clean geometric results
Summary
A second crucial point for significantly reducing manual efforts in an image-based 3D digitisation pipeline is background masking This operation is frequently necessary (i) when a turntable is used, and the background is static, (ii) for jointly processing images where the artefact has been flipped (e.g. front and back side) or (iii) to limit the area where Multi-View Stereo (MVS) algorithms are applied to decrease the computational time and unwanted 3D points. Learning-based methods were presented for point cloud denoising (Duan et al, 2019; Hermosilla et al, 2019; Erler et al, 2020; Luo and Hu, 2020; Rakotosaona et al 2020; Luo and Hu, 2021) Promising, these techniques are frequently sensitive to outliers and generally fail with a high level of noise in the data.
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