Open Room Studio – Setting the Scene

As discussed and seen in Chapter 3.8, the object to be photographed was placed on a pedestal in the room so as to allow the camera, which had been attached to a tripod, and the camera operator, free movement around the object (Figure 3.23). Any reflective surfaces were covered so as to eliminate any glare which stray light might cause by reflection. The furniture in the room was also moved so that it did not impede the camera movement, but at the same time it was best placed so as not to have to reposition them again, as both the subject and surrounding furniture were required by the computer software, in order to stitch each frame of the digital images together to form the final 3D virtual image.

Figure 4.1: Data Capture - Advantageous camera angles.

Any movement of background objects might cause a mismatch of the image data stitching process The Clay Head, seen in Figure 2.18, one of many of the objects to be photographed in this thesis, was the first object to be processed, and is seen resting on a flat table top. On later photoshoot sessions, it was found much more advantageous to place the artifact on a raised upturned flower pot (Figure 4.1). This allowed for the camera angle to be well below the horizontal plane (see arrow A) and thus to capture more detail from this obtuse angle (see arrow B).

A

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Figure 4.2: Data Capture - Supporting model above horizontal plane.

By raising the artifact above the horizontal plane, as seen in Figures 4.1and 4.2, more detail was obtained, for example, from the underside of the protruding ridge (see arrow C, Figure 4.2), improving the quality of the digital data image and therefore the finished surface detail of the replicated model.

Figure 4.3: Correlation between camera position and horizontal image deviation.

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Figure 4.4: Four frames from part of the Data set of 75 photographs. Figure 4.3 shows the camera positions seen from the horizontal plane, A1– D1 representing the different angle plane of each photograph, as clarified in Table 4.1. Figure 4.4 shows four frame which form part of the data set of 75 photographs that were sent for processing via the internet cloud using 123D Catch® software.

Table 4.1: Correlation between camera positions and horizontal image deviation.

Camera position A 90° above Horizontal plane photographic capture position A1

Camera position B 35/40° above Horizontal plane photographic capture position B1

Camera position C Horizontal plane = 360° photographic capture position C1

Camera position D 35/40° below Horizontal plane photographic capture position D1 4.1.2 Data Capture – 123D Catch®

Using 123D Catch® software, as discussed in Chapter 3.8, 24 objects were photographed both indoors and outdoors, and processed using this software. Those data sets that were taken indoors used the basic method, as described above (Chapter 4.1.1) the photographs taken outdoors were taken as described in Chapter 3.7.2.

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Using 123D Catch® as the primary digital software, the research established a procedure as tabulated below, but it must be emphasized that this might not be the appropriate procedure or methodology when other data processing software is used to process the digital images (Figure 4.5).

Figure 4.5: 123D Catch® Data processing Flow chart.

To date, the research has identified a procedure in keeping with the aims and objectives of simplicity. The software program used for the processing showed, on several objects, digital photogrammetric problems, which have been identified but have yet to be addressed and overcome. It has been found that even at the lowest lighting conditions, excessive glare or flare can be present on the digital image, and this can be the cause of distortion. The surfaces of several of the original objects have been found to be too reflective, causing the digital image to be distorted.

The flow chart, seen in Figure 4.5, shows seven major stages in which digital data is captured by use of a DSLR camera (or other) to produce from sixty to seventy-five *.jpg images which are then imported into the primary digital software. The individual images can then be checked for quality and sent via internet cloud technology to be processed. As Verhoeven [70] points out, the time taken for this process is dependent on the quantity and quality of the images, (as

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well as internet speed) but a reduction in either can result, as Nguyen et al. show [73], in processed image data which is badly degraded.

Figure 4.6: Close-up screen shot of image waiting to be cleaned.

Having retrieved the point cloud image, filtering or cleaning of background clutter/noise of the image was required (see Figure 4.6 and Figure 4.7). Many of the original artifacts were placed on a patterned cloth (or even newsprint) which covered the plinth on which the artifact was placed.

Figure 4.7: Clay head identified in blown up image amongst background “noise”. Surface covering to be removed/cleaned Surface covering to be removed/cleaned

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This cloth, or covering, was then removed from the returned processed image, simply by highlighting the area and pressing the delete button within the software indicated.

As will be seen in Chapter 6, this patterned cloth helped in the repair of incomplete data.

The file was then ready to export as an *.obj file into secondary software programs, in this instance StudioPro®. This created 3D textured mesh which could be further repaired or enhanced. Figure 4.7 is a complete frame of the returned data file before it was cleaned.

This technique was presented at the ESDA 2014 Biennial Conference in Denmark and later published by ASME (see Appendix A) [161].