Several forms of visualization were used to encode the values of the database. To provide a basis, the actual remains of the monument were recorded with a Canon Eos 600D and a dji drone. The images from the dji drone were provided by the University of Nijmegen
and the “Mapping the Via Appia” project. As a consequence, the photographs were
processed in Photoshop and corrected in color, brightness and contrast. After that, Photoscan calculated a 3D model from the available data. The reality-based model, which was derived from photogrammetry, had to be decimated in order to further investigate
in its geometry. Currently, the polycount blasts most computers’ hardware. The context and later the audience facilitated the use of the automatic decimation method instead of manual repairing, retopologizing and remapping the uv-coordinates19. As a next step, the reconstruction of the ancient structures was modeled by means of polygonal modeling in Blender, with the assistance of the reality-based model and the databases. Therefore, scaled blueprints from different perspectives were prepared in Corel Draw to enable endless zoom factors. They were saved in vector files and aligned to the virtual spatial axis of Blender. The visualization itself happened with Cycles, a build in render engine20 from Blender.
However, the main purpose of this chapter is to explain how the uncertainty was encoded into the visual elements of the final rendering. If possible, parts of the discussion of the case study also had to be considered an important aspect of visualization. Considering malfunctions such as color blindness, not all available methods seemed to be suitable for this exploitation. In some cases, slight adaptations were necessary. The approaches themselves were presented and applied in three categories: color code, render mode and level of detail (tab. 5 in Chapter 2). During each attempt, an effort was made to retain the same geometry in order to reconcile the subsequent evaluation of the results. Additional, binary systems, which often appear in publications, were neglected. They are only capable of deciding between actual remains and the interpretation, which did not correspond with our own conditions.
19 Common steps to enhance 3D scans for a real-time render engine or extensive post-processing.
Roughly, they optimize the model and correct errors in the geometry.
87
The first method relates to color coding and mostly refers to section “2.3.3 Visual
representation”and “2.7.2 Color”, but it also includes approaches from “2.7.1 Fuzzy logic”. Color coding means that certain color values are assigned to the individual objects, depending on their uncertainty. The color palettes (fig. 41) themselves were generated with the help of www.colorbrewer2.org and www.paletton.com. For the fuzzy-based uncertainty concept, a color gradient from two complementary colors (fig. 41) was used because of the diverging data. Instead of complementary colors, analogue colors can also be used, since they stand for sequential data, which also applies to fuzzy-based uncertainty. Nevertheless, complementary colors are more suitable for emphasizing extreme points of scale. Furthermore, the gradient is robust against most forms of color blindness and approaches the often used red/green contrast. In order to justify the level of detail of the interval [0,1], the transition between the colors was kept smooth. A similar approach was used by Zuk et al., who used a transparency gradient to display temporal uncertainty (Zuk et al. 2005). By contrast, another system appears to be more suitable for the class-based uncertainty concept. The data was more divergent, so four complementary colors (fig. 41) were used in order to represent the four source categories. In this case it was not possible to choose complementary colors that were also robust against color blindness. However, three of them still represent primary colors and can thus be easily distinguished from each other in the natural environment. The concept on
which they are based is the extended triadic and is called a tetrad. Dell’Unto et al. in
particular use the concept of contrasting colors to represent varying categories of
uncertainty (Dell’Unto et al. 2013). Both color palettes were used to encode the
uncertainty of one possible reconstruction, regarding its form and dimensions.
Figure 41: The top part represents a color gradient from a brownish (#543005) to a greenish (#003c30) tone, passing over a neutral white (#f5f5f5). The certainty increases from left to right. The bottom part represents the four complementary colors of the tetrad and its related source categories. Red (#aa3939): literature; Yellow (#aa9739): analogy; Green (#2d882d): fieldwork and blue (#403075): illustration (Brunke 2017).
The second method corresponds to the render modes and mostly refers to “2.3.4 Style
88 form of shading provided by most cad software. In this case wireframe, solid, texture and material (fig. 42) were used. Hereby, the certainty increases with each step; the details also increase proportionally. Therefore, wireframe (abstract/schematic) mode represents the lowest certainty, whereas the material and texture mode (photorealistic) represents the item with the highest certainty. The lineup here may imply similarity to crisp sets which it does not necessarily mean. The uncertainty acts anti-proportionally. Even if the appearance changes a lot, the geometry remains the same. Since the changes are stepped and follow each other in a relative order, the same template can be used for the fuzzy- based and class-based uncertainty. The results might be similar but the threshold can be slightly shifted. This method has a special strength against color blindness because it is not dependent on color rendering and color truthfulness. This approach is not used much in the literature. However, there are portioned approaches to this in various publications, so Hermon and Nikodem use the wireframe mode for uncertainty levels above a certain threshold of uncertainty. All other objects are displayed in the solid mode, combined with a color code (Hermon and Nikodem 2008). In contrast to that, Alusik and Sovarova used simple material textures to indicate the probable material of the reconstructed structures (Alusik and Sovarova 2015).
Figure 42: Different render modes of the same cube with an increasing certainty from left to right: Wireframe, solid, texture and material. The material is achieved by using physically-based rendering texture maps (Brunke 2017).
The third and last method corresponds to the level of detail. In contrast to the previous ones, it will not have much impact on visualization since this monument does not allow much variance in its detail and has a quite homogenous degree of uncertainty in its outer form. However, this means that the higher the certainty of specific parts the higher the details of the model. The lowest uncertainty will be presented as a reality-based model of the actual appearance, whereas the highest uncertainty is only a schematic box model, bounding the parts that might have been there. Method three has intersections with method two, since both involve a manipulation of details. The difference is that method two does not touch the geometry, whereas method three only manipulates the geometry.
89 In total, three versions of the evidence-based reconstruction were produced. One was done using the solid mode mixed with a color code, for another one the render mode was used, while for the last one the level of detail was mixed with a photorealistic style. All three variations required manipulating different properties of the model in order to
display the object’s uncertainty. Finally, these approaches were evaluated and used to
develop a final method. However, before doing so one had to decide about an appropriate presentation of the model. The selected method is described in the next chapter.