When focusing on the 3D model instead of the datasets, not all theoretical results and blueprints are as accurate as previously thought. They are not faulty; they rather have minor deviations in their dimensions. The aberrations most likely emerge because of the non-uniform body of the monument. Within the core those deviations are hardly recognizable. However, the case is more sensitive. To highlight each case layer, a slight bevel has been added to the edges of the reconstruction.
More related to uncertainty is the level of detail. Considering past discussions among scholars, a moderate level is used rather than a detailed one. With decreasing details the uncertainty might decrease as well, but so will the information density. Hence, a correct balance must be found. There is no benefit in imitating the reality one by one or simplifying it until no information is left. One promising way could be to use the highest measurement inaccuracy of the object as a minimum for the detailed appearance in the monument. However, the level of detail is part of the discussion about visualizing uncertainty and will be discussed in the corresponding paragraph in detail.
Secondly, what part of the model is going to be rendered (fig. 57)? Interactive simulations and animation can render everything. For interactive simulation, the observer can even decide which layers should be visible. In contrast, images can show only one perspective. Additionally, objects can cover other objects or face away from the camera. One possibility is to filter the objects that are displayed. Only the interior or exterior parts can be shown. Enforcing a filtering is not optimal because it might filter out inaccurate segments as well. Another possibility is the use of transparent shaders to enable a look through. However, this aspect also belongs to the field of visualization and should therefore be discussed there. It could be confused with the visualization of uncertainty.
109 Figure 57: Multiple renderings of the same segment in different variations and separated from its total context. This example shows four variations of the roof: mound, cone, plane and none. All renderings in high resolution are available from Plates 11 to 18 (Brunke 2017).
Thirdly, one could provide several renderings from different perspectives and using different segments (fig. 57). The advantage is that alternative versions can be displayed without any difficulty. The disadvantage is that many images have to be rendered, making it difficult to recognize the overall picture. In total, an image rendering is always a reduction of possibilities and information and should be avoided whenever possible. However, not disturbing the overall context, neglecting specific parts or using visualization methods, one can remove some of the geometry in the form of a cross-section. Rather than cutting the complete monument in half, only up to a quarter should be removed to expose a partial cross section. Since the object is often symmetrical in Roman contexts it facilitates insight without losing too much information. Nevertheless, only one variation at the time can be rendered. This leads us to the next issue. Assuming only one variation can be displayed, which one should be chosen? A change in variation also means a change in uncertainty. Usually many different alternative versions are available, but only a few suits the context. Basically, each source projects its own version into the room. Instead of modeling all of them, it is better to assess the approaches based on their uncertainty and elaborate the reconstruction with the least degree of uncertainty. Additionally, some extremes might also be good to indicate the possible range of alternative versions. Each of the variations needs its own uncertainty. Aspects such as material, building technique, form and dimension are available. The form and dimension fit best as overall uncertainty, since it is the spatial data that is displayed initially.
Furthermore, exact and three-dimensional spatial data is difficult to implement on a conventional database. One reason is the irregular nature of the heavily weathered and changed object. The number of corners and transitions makes it impossible to transfer it to a clear set of quantitative data. It also complicates the current measurements of data, which results in measurement inaccuracy and further compromises the reliability. To minimize the impact of spatial measurement uncertainty a scale and detailed documentation might be beneficial (fig. 58). In this context, it is also important to point out the currently described uncertainty and how it is encoded into the model.
110 Figure 58: Concept drawing of the theoretical approach from uncertainty in 3D models, by adding a definition and scale. The inaccuracy provides the measurement accuracy of the reality-based model, while the approximation returns the rounding factor. The uncertainties are stacked according to their priority and visualization. Finally, the location of the most important data is announced. The graphic can be superimposed on the final rendering (Brunke 2017).
However, it is problematic to place a two-dimensional scale in a three-dimensional space as an overlay because of the missing perspective and spatial information. Instead of using a 2D overlay, perhaps put them directly into the model. Furthermore, issues also arrive with the colors in the legend. The light of the rendering or reflections might bias the perception. Too much or too little light may even clamp the colors.
In general, there are still many problems with 3D modeling, namely the inaccurate data, the blocking view, the level of detail, the choosing of the right segment and the description of the results. Although all of these problems have been identified so far, they must be dealt with and solved individually. An attempt will be made to do so in the following sections by discussing different forms of visualizations.