Automatic reconstruction of Roman housing architecture

P. Müller Computer Vision Laboratory, ETH Zurich, Switzerland

T. Vereenooghe ARTS, K.U. Leuven, Belgium

A. Ulmer Computer Vision Laboratory, ETH Zurich, Switzerland

L. Van Gool Computer Vision Laboratory, ETH Zurich, Switzerland; and ESAT, K.U. Leuven, Belgium

ABSTRACT: The creation of 3D models of ancient sites has often been focused on their major monuments only. This is logical, given the high costs of traditional 3D modeling. Thus, to effi-ciently reconstruct entire sites like cities, a large number of domestic buildings and workshops need to be generated automatically. Their appearance should follow the aesthetic and statutory architectural rules of the corresponding epoch. From various GIS (Geographical Information Systems) data given as input, such as population density, land usage, street network and build-ing footprints, our system assigns type and style of the buildings to its footprints and calls the corresponding shape grammar rules to efficiently create detailed large-scale models. The shape grammar rules which are responsible for the creation of the actual building geometries are manually derived from photos and plans of remaining buildings, archaeological excavation data, and (historical) paintings. To complete the model of the reconstructed urban zone, streets are automatically embedded according to the given GIS data and appropriate vegetation is added based on simple procedural rules.

1 INTRODUCTION

Interest in 3D models of archaeological sites is quickly increasing, as people get used to such models in the context of games and Hollywood blockbusters. Such models do not only provide for edutainment however, they can also become a powerful hypothesis verification tool when in the hands of scholars. Even if graphics related technology has made impressive advances over the last years, building large-scale models with a high degree of realism remains overly expen-sive. The huge budgets behind games and movies are not available to any excavation project. Thus, 3D modeling is usually confined to the more `interesting’ monuments of a site. Yet, to give a good feel of how the environment of the former inhabitants was, complete site models are called for. This paper describes a framework to do so, at moderate cost. The framework is based on using grammars to automatically generate buildings of a prescribed architectural style and tallying with prescribed constraints like known footprints. In particular, the paper exemplifies this approach for the famous Pompeii site. As a disclaimer, we want to emphasize that the 3D models shown in this paper have not gone through the necessary scrutiny by expert archaeolo-gists yet. Collaboration with Pompeii experts is being discussed at the time of writing.

1.1 Related Work in Urban Modeling One approach to modeling existing cities is the use of GIS data (a.o. building footprints) and ae-rial imagery (e.g. Takase et al. 2003, Ftacnik et al. 2004, Scholze et al. 2002). These systems rely on highly calibrated aerial images as the main input for height determination and roof gen-eration of buildings. With those systems, impressive results have been generated, but they are unsuited for the reconstruction of ancient ruined cities, and the textures on the facades, where still available, have to be added manually or through mobile mapping procedures.

In architecture, shape grammars (Stiny & Gips 1971) were successfully used for the analysis and construction of architectural designs (Stiny & Mitchell 1978, Koning & Eizenberg 1981, Flemming 1987, Duarte 2002). Shape grammars use production rules, which are defined di-rectly on shapes (labelled points and lines). These rules iteratively generate a design by creating more and more detail. The derivation is usually done manually, or by computer with a human supervising the rule application.

Wonka et al. (2003) successfully introduced shape grammars to the computer graphics com-munity by making them more amenable to computer implementation. In their approach, build-ing designs are derived using a parametric set grammar. Their production rules consist of geo-metric split operations, which hierarchically subdivide the façade structure. They generated buildings with rich geometric detail for several different architectural styles.

1.2 Our Approach We present an extended version of the CityEngine system introduced by Parish & Müller (2001), which is capable of reconstructing a complete ancient city using a comparatively small set of archaeological input data and is highly controllable by the user. To the best of our knowl-edge, there is no other such system available, although a very similar project is presented in (Birch et al. 2001), where specific architectural objects have been parameterized to allow the rapid procedural modeling of archaeological content. In their approach, the creation of individ-ual buildings is already supported using parametric modeling. However, there is still a lot of manual work needed to create a whole urban area.

While the CityEngine presented in (Parish & Müller 2001) was able to create virtual look-alike cities, we here extend the system with the capability of importing real building footprints and creating building geometry tallying them, using the latest techniques in procedural model-ing of buildings (Wonka et al. 2003). Although we demonstrate our framework here for Roman architecture at Pompeii, the main design goal is easy extensibility. One can add new features, such as completely different architectural rules and alternative land uses. To achieve this, the system was designed in an open way, capable of dealing with higher-level mechanisms like grammars.

1.3 Overview In section 2 we describe the concept and the pipeline of the system, and the methods used therein. In section 3, a brief overview of relevant Roman housing architecture is given, and, based on these architectural guidelines, shape grammar rules are derived to define the actual building geometries. We use the ancient city of Pompeii as test scenario. Section 4 explains how streets and vegetation are generated to complete the city model. The results we achieved are shown and discussed in section 5. Finally, our conclusions and plans for future work are given in Section 6.

2 SYSTEM ARCHITECTURE

The system consists of several different tools which form three pipelines (illustrated in figure 1). All three pipelines are divided into two steps: attribute assignment and geometry creation. In the first step, the raw GIS input data is prepared by automatically assigning attributes to the corre-sponding entities, e.g. determining the desired architectural style for each building footprint in the first pipeline. Optionally, the user can take corrective actions by modifying or overwriting these automatically assigned attributes. The second step, which needs additional specific input data, takes care of the actual geometry creation. The first step of the vegetation pipeline repre-sents an exception in that it also fills in missing information on plant positions, which is not pre-sent in the GIS data. The geometry outputs of all three pipelines are merged into one final 3D model, which then can be rendered either in real-time using OpenGL or offline by using com-mercially available render packages like RenderMan, Mental Ray or Maya.

Figure 1. The three pipelines of the city modeling tool. The white rectangles represent input data, the grey boxes list individual processing units, and the black box illustrates the output of the system (which is a 3D model of a city).

The GIS input data to reconstruct the ancient city is represented by 2D grayscale image maps

and vector data. Those image maps control the attribute assignment step of the pipelines and thus the behavior of the whole system. They can easily be generated either by drawing them or by scanning from statistical and archaeological maps. For Pompeii, such maps can be found in (Etienne 1991, Peterse 1993); additional sociostatistical information is available in (Wallace-Hadrill 1994). The following sociostatistical maps are needed: (1) population density, (2) an age map of the urban areas to specify when the quarters have been built, and (3) a functional map to describe the land usage. Figure 2 shows examples of such maps. We distinguished between 5 types of land usage: domestic, shops, hotels, public and agriculture. The streets and building footprints are represented as vector data which can be created within a commercially available GIS package like ArcView from scanned archaeological maps.

Figure 2. Sociostatistical maps are used as input for the system. Top left: population density. Top right: age of urban area (darker areas mark older parts of the city). Bottom left: functional map (gray tones for each zone, e.g. domestic zones are marked white). Bottom right: overview map of ancient Pompeii for comparison.

3 CREATING ROMAN BUILDINGS

3.1 Roman Housing Architecture Due to the good preservation of the city, the domestic architecture of Pompeii has been studied extensively (e.g. Eschebach 1979, Richardson 1988, Wallace-Hadrill 1994). The Pompeian ar-chitecture spans several centuries and the buildings exemplify the evolution of domestic archi-tecture: from the Italic model of the 4th-3rd centuries BC to that of 1st century AD Imperial Rome. The form of the Pompeian domus or townhouse is derived from Greek and Hellenistic designs and varies greatly in size and elaboration, from two or three rooms to large buildings with many rooms arranged around courtyards.

The houses were built in a wide variety of shapes and sizes, but they usually displayed the preference for symmetry around an axis that characterizes most of Roman public architecture as well. The city was divided by the streets into blocks (insulae), and the houses were built con-tiguously. The wealthiest city dwellings might occupy an entire block, as did the so-called House of the Faun at Pompeii, built early in the 2nd century BC. Other insulae contained a dozen of separate houses.

Before the 1st century BC the town was inhabited by Samnites who conquered Campania in the end of the 5th century BC. They were besieged 89 BC by the Roman general Sulla, when Pompeii became a real Roman city. Thus, most of the architecture can be considered as pre-Roman.

The oldest houses can be dated in the 4th century BC. In this period the houses consisted of a central atrium, surrounded by several rooms. The ideal layout of the atrium house is typical and widespread in Italy, but this layout knows many variations to this rigid scheme. In the 2nd cen-tury BC, the most elegant houses in Pompeii were built, reflecting the influence of contempo-rary luxurious Hellenistic peristylium houses. However, the newly introduced peristylium took a different function: it was transformed into a garden, giving access to various rooms. The peristylium was carefully laid-out with in its centre a (four-sided) colonnade and a variety of plants. The large number of houses built in pre-Roman times, made it unnecessary to build many new ones in the Roman period. Roman houses were usually less imposing, with lower atria, but with more elaborate decoration.

3.2 Shape Grammar Our shape grammar is based on the split grammar introduced by Wonka et al. (2003). Their

approach uses subdivision as the basis for design, and is therefore well suited for the description of façade structures. A split grammar consists of shapes and split rules which geometrically de-fine how a labelled shape is subdivided into a set of other labelled shapes. A model is derived by subdividing shapes using split rules until only terminal shapes remain. Those terminal shapes are then replaced by geometry to form the final model. Figure 3 illustrates this derivation proc-ess on a Roman façade. For example in figure 3 on the left, the shape facade is split into the two shapes with label floor (in the sense of storey).

Figure 3. First 4 steps of a shape grammar derivation sequence. On the right the result of the derivation.

A disadvantage of split grammars is that they are primarily suitable for rectangular designs

like façade structures. To be able to handle the non-rectangular building footprints, we extended

the grammar with split operations for general polygons. Also, split grammars are limited to a hi-erarchical subdivision of designs. But often, additive processes are needed for the generation of designs, especially for the creation of building shapes. We therefore added the branching and transformation mechanisms of L-systems (Prusinkiewicz & Lindenmayer 1990) to our shape grammar. Due to the simple structure of domestic Roman houses, we don’t need a control grammar (Wonka et al. 2003) to manage the variation of the different building styles. Instead, our shape grammar generates the desired variety by using probabilistic derivation rules.

We created shape grammar rules for three different types of buildings: houses, shops and ho-tels. Based on the functional map in the GIS input data, the rules are applied on the correspond-ing building footprints. Moreover, each rule is influenced by the age map and the population density map. In the following two subsections, we will present the important properties of Ro-man housing architecture in terms of composing shape grammar rules.

3.3 Building Shape To create the shape of the buildings, the footprints are extruded by the corresponding shape grammar rule. The height of the extrusion is determined by the age of the region and the popula-tion density. For example, a rule applied on a domestic building in a high populated area of the city, tends to generate a two-storeyed building of a height between 6 and 9.5 meters. A lower population density entails the shape grammar rules to generate more one-storeyed buildings (of a height between 5 and 6 meters).

To cover the buildings, different types of roofs can be generated by the shape grammar rules. Most of the buildings in Pompeii had pitched roofs with variations in gable, mansard, or hip (having four sides sloping from a short ridge or center) form. In areas like in the South of Italy, where the shedding of rain and snow does not present a problem, roofs may also be flat.

While most of the buildings can be generated using the simple extrusion mechanisms, the generation of the peristylium represents an exception. A peristylium consists of two footprints: one representing the outer façade and a second one describing the inside colonnade. We there-fore labeled these footprints in our input data and wrote a shape grammar rule for peristyliums, which generates (1) the outer façade, (2) the inner colonnade, and (3) the appropriate sloped roof.

After the creation of the shape, the rules for the façades are called on each side of the gener-ated extrusion. The sides are labeled with tags describing their orientation, e.g. the tag with the label street is set if the side is located directly on a street.

3.4 Façade In general, houses in Pompeii had monotonous façades, sometimes with projecting second sto-ries or balconies. The houses were characterized by an inward orientation, with only few and small windows in the exterior walls. Light entered the building mainly through the atria and peristylia. Exterior windows were small and barred against intruders. Some of the windows had iron gratings, a few had opaque glass. Sometimes windows have a wooden frame

The wooden entrance doors were remarkably large in size, often 4 or more meters high. Most doors are quite similar: they are double-leaved, with a fairly simple carpentry. They were some-times aligned with protruding pilasters. The urban house might include also shops opening onto the street e.g. the House of the Tragic Poet). Grooves indicate that shops were closed off by slid-ing wooden doors.

The exterior walls were often covered with (painted) plaster. Sometimes the plaster was marked and painted to resemble stone. The lower parts were often painted in a reddish color. Some of the houses also had external staircases, i.e. staircases accessible directly from the street. Other architectural elements included wooden porches above windows or doors.

Figure 4 shows three exemplary façades, as generated with the shape grammar. The left fa-çade represents a (closed) shop. Owners of a shop often lived on the first floor, above their shop. The second façade also belongs to a two-storeyed building (hotel), with a more elaborate deco-ration of the door. The picture on the right shows a façade of a simple, one-storey house.

Figure 4. Façades generated with the shape grammar. The width of each façade is 7 meters.

4 MODELING OF THE ENVIRONMENT

Once the geometry of the buildings is generated, the system creates models of the surrounding environment, i.e. streets and vegetation. These secondary objects increase the quality and com-plexity of the virtual city model, and thus enormously enhance the realism of the scene.

4.1 Streets The streets of an ancient Roman city show different widths and different accessibilities to wheeled and pedestrian traffic. Important thoroughfares measured normally between 5 and 7 meters in width (including sidewalks). In Pompeii, the widest street had a width of 9.5 meters. Secondary streets, like in the residential areas, may have been between 2 and 4 meters (Adam 1984).

At Pompeii, the paving of streets may have started from the first half of the first century BC onwards (Eschebach 1978). At the time of the volcanic eruption in 79 AD most of the street is Pompeii were paved with large polygonal lava blocks, as can still be seen today. Some streets had no pavement, mostly in the southeastern quarters. This was probably a deliberate decision (Gesemann 1996). Most streets in Pompeii, even the narrow ones, had sidewalks (margines) on both sides, ca. 30 cm above the level of the street. On average both sidewalks account for half of the overall width of the street. Pedestrians could cross the streets by means of huge stepping stones, which allowed the passage of high wheeled vehicles. The water supply and drainage of the city didn’t play an important role in the street layout (Ohlig 2001). In ancient Roman cities the often well finished and closed off supply and drainage channels frequently ran underneath the surface. Only at narrow streets the drains were positioned as an open channel along the edge of the paved surface.

While the width of streets and sidewalks can be read from the given GIS input data, the other street attributes (pavement, open drains etc.) are assigned automatically by using simple para-metric and probabilistic models. For example, if a street wider than 4 meters is located in a quarter built after 50 BC, the street is paved and has drains below grade. After the assignment process, the model can be generated using the corresponding street layout. The latter is given by the user as input and consists of the profile, the texture and optional supplemental elements. The geometry is then created by (1) extruding the profile along the corresponding street path, (2) as-signing the appropriate texture to simulate the surface material, and (3) inserting the supplemen-tal elements, e.g. the stepping stones.

It has to be mentioned, that street layout is never an isolated enterprise. Therefore, features of layout should be considered in relation to their chronological framework, for example the ex-pansion of the urban area within newly arranged urban quarters. To satisfy this unpredictability, the user can manually overwrite street attributes using either the CityEngine user interface for local modifications, or image maps (embedded in the GIS data) for global modifications.

4.2 Vegetation Vegetation and especially gardens seem to have played a significant role in Roman life. The de-sire for a bit of green appears to have been an intrinsic part of the Roman character. Jashemski (1979) concluded that the garden was a key part of the Pompeian house, whether large or small. The elite in Pompeii often had several large peristyle gardens, while even the poor made places in their modest homes for tiny gardens. Besides these gardens with a private character, gardens were also often found in public places. In Pompeii, excavations revealed various large and im-portant cultivated areas within the city, particularly in the southeast part.

A simple grammar has been developed specifically for distributing vegetation in urban areas. It allows for an abstract description of vegetation: different mechanisms support both accurate and randomized placement. Zone subdivision and layered occlusion extend the possibilities of the distribution. Access to a configurable model library offers control over the selection of plant instances with automatic variation and detail levels. By combining these features a collection of hierarchical, reusable rule sets is created for the desired vegetation styles. A cityscape is subdi-vided into different vegetation zones which trigger corresponding rule sets according to their function: Avenue zones are created along streets and provide contiguous regions over several blocks. Block zones are regions enclosed by streets and are meant to represent bigger park ar-eas. A lot zone contains one building parcel and the knowledge of footprints allows the vegeta-tion to interact with potential buildings.

To build the library of plants, we used the commercial tool xFrog from Greenworks. The core of the generation process is based on L-System rules, which grow models in an almost natural way (Prusinkiewicz & Lindenmayer 1990). The software allows the creation of realistic plants in different states of aging, which can easily be varied by changing seed parameters. Several li-braries with elaborated and biologically correct rules are available for xFrog. For the visualiza-tion of ancient Roman cities the Mediterranean set was used, including 20 models in 3 different ages (see figure 5).

Figure 5. Example models of myrtles generated within xFrog. On the left two full-grown shrubs (ap-proximately 5 meters high); in the middle two examples at medium age (2.5 meters high); and on the right two young myrtles (1.5 meters high).

5 RESULTS

As a test case for the procedural modeling framework described in this paper we chose a domes-tic quarter in the ancient city of Pompeii. The procedure proved to be very successful in its practical application. Both the speed of execution as the quality of the resulting model are clear advantages of this approach.

After an intensive study of the Pompeian urbanism and domestic architecture, a complete three-dimensional model of Region 6 could be generated in a relatively short period. Defining the shape grammar rules, building the model and rendering it took no more than 24 hours. Fig-ure 6 shows the snapshots from the reconstructed model. The geometry of the buildings consists of 2.4 million polygons and the rendering was done within Maya.

Another inherent advantage of this approach is that the resulting 3D model is by no way con-clusive. This is certainly an important issue when virtual reconstructions of archaeological sites are created. Within this framework archaeologists can easily test and compare alternative hy-potheses. Moreover, when new information about the architecture becomes available, the model does not become obsolete, but can easily be adjusted by updating the grammar rules.

6 CONCLUSIONS AND FUTURE WORK

We have presented a system that is capable of reconstructing a domestic urban zone of an an-cient Roman city based on GIS-input data. Detailed large-scale models were efficiently gener-ated through a combination of real world GIS data usage and procedural modeling. In contrast to existing procedural systems which generate only virtual look-alike cities (e.g. Parish & Müller 2001), our approach takes account of the knowledge that is available about the site. It complies with known building footprints, for instance, and adapts to demographic patterns. Compared to the traditional way of modeling urban zones using commercial tools such as Maya, our system decreases the manual effort substantially, due to (1) the procedural nature of the buildings generation process, and (2) the intuitive control possibilities on all levels in the hierar-chical system.

Several further improvements to the software will be considered: Complexity of the modeling task : Modeling architecture with grammars is difficult and requires some experience. Nonetheless, the process could be facilitated by extending the grammar deri-vation with the feature of combining and reusing rules (of e.g. other architectural styles). In combination with a library of predefined rules and an elaborated user interface, this ‘reusability of designs’ has tremendous potential for an interactive architecture modeling editor. Modeling façades on scanned building shapes : 3D models of urban scenes, which have been automatically generated using computer vision methods (e.g. Takase et al. 2003, Frischer & Guidi 2005), show very accurate building shapes. But often, the façades couldn’t be recon-structed in detail. A shape grammar, consisting only of architectural rules for façade creation, could be applied onto these automatically acquired building shapes. Interior design of buildings : Having the complete interior with furniture would allow a virtual tourist to enter and walk around in the reconstructed buildings. The shape grammar mechanisms could be developed by taking into account the therefore needed floor plan and interior design. This 2d architectural problem corresponds to a partition process of constraint satisfaction (Maculet 1991, Harada et al. 1995); but unfortunately, no comprehensive procedural modeling approach for interior design has been presented yet. Populating the city with avatars : Another logical step would be to add virtual humans to the site, who performs actions and behave in accordance with the selected time period. Such popu-lated scenes have already been demonstrated (e.g. Papagiannakis 2004, de Heras 2004).

ACKNOWLEGMENTS

The authors gratefully acknowledge support by the EU IST NoE EPOCH, and the STREP Cy-berwalk. Furthermore, we would like to thank Simon Hägler for helping with the façade models.

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Figure 6. The resulting 3D reconstruction of a domestic area in Pompeii. For comparison, on the top right a painted reconstruction of an urban area in Pompeii scanned from (Coarelli et al. 2002).