Longhouse 1.0 began in the Winter of 2013 through a series of discussions with long-time 3D animation and VFX collaborator Andrew Alzner as a starting point for my Ph.D. research into Phenomenological experiences within virtual environments (see my blog on methodology & research). Both Andrew and I had met each other in 1996 at Side Effects Software (SESI) and had travelled to Japan and LA regularly for customer support. SESI was one of the three original Animation and VFX software companies founded here in Canada which dominated the animation and VFX production industry. SESI was know for it’s procedural animation methodology, which would allow users to build 3D objects, animation or VFX sequences through a dynamic, interrelated and real-time pipeline through a software application called Houdini. Basically you built a 3D object using operators that represented a single stage in the modeling process. If you changed one operator, the changes would ripple through all of the operators essentially creating a living document of the model one was making in 3D.
Procedural 3D Modeling is a dynamic building block technique for organically creating digital assets. The proposed pipeline has been specifically designed to allow stakeholders (public, private, academic and descendant) to access a procedural 3D model library in order to build in real-time and within 3D space, interactive visualizations of extant cultural heritage structures. Beyond initially allowing users to “build” their own archaeological engagement, stakeholders are able to experience the association between the physical structure, spatial relationships and the phenomenological experiences of these archaeological landscapes. These built digital assets can also be reapplied within any numerous engagement tools such as mobile Apps, Internet Websites or even within 3D gaming engines, further extending the narrative beyond the individual’s brief but personal archaeological experience.
In simple terms, procedural modeling is a process in which all of the steps needed to create an object in 3D are held in a dynamic relational network of building blocks that allow the user to alter, change or experiment with the final model at any stage of the building process. As in this example, a picture of a pot is superimposed in the display window. A NURBS spline is built by placing points along the outline of the pot and then a new procedural operator called a “revolve” skins that single outline spline 360º creating the 3D surface. Finally a transform operator is inserted within the middle of the procedural network and when one parameter changes, that change affects the relationship of the next modeling operation within the network, causing the model to alter accordingly.
It is possible to use this methodology to develop a process in which the archaeological landscape can be methodically reconstructed while retaining the ability to experiment with the assumptions in near real-time visualization. Further, once the method is in place, the technology can then be packaged in such a way to allow for more pre excavation or during excavation interpretations, stakeholder or public engagement and further research.
Using this concept of total user control, we started to develop a dynamic pipeline for the creation 3D longhouses using the SESI Houdini procedural method. We first started with a standard post-excavation site report map. Working with ASI (Archaeological Services Inc.), they provided an example in PDF form, which was then inputted as a base image into Houdini.
Using the post hole positions and selecting a certain diameter range used on the site map, we spawned simple 3D models of poles for every post hole. Essentially we “birthed” posts where they were recorded from the archaeological data provided. This allowed us to visualize the initial positioning of the poles and how they related to each other in 3D space.
This process was repeated using the same technique, but this time larger pole diameters were selected in order to differentiate the mixed used of pole sizes recorded within the archaeological record. What we were attempting to do was create an automatic pipeline that would size pole diameters from the field mapping and then cluster and group poles of equal diameter and position.
This technique worked well on site plans that had been prepared so that post positions was the only data being detected. However, substantial labour intensive work had to occur with the raw 2D data for this technique to work. After discussions with Side Effects Software, they prototyped an additional procedural modeling network that would allow any site plan to be imputed with post points being detected, isolated and converted into 3D posts. The notion was to allow non-3D users to be able to pick any site plan material and upload it into the pipeline to be able to create their own 3D model.
Although much slower in real-time, the process proved successful in pre-post point selection and modeling. However, it was abundantly clear that if the public was to use the system, extensive 2D map clean-up had to occur first to allow for a faster visual experience.
In an attempt to refocus the process more for archaeological research needs and after discussions with Dr. John Creese, we wanted to test his Kernal density estimation (KDE) analysis post-clustering theories using this technique but with another popular 3D animation software application called Autodesk Maya. Working with Toronto based VFX Supervisor Mahmoud Rahnana we took a site plan from John’s 2009 paper entitled; Post Molds and Preconceptions: New Observations about Iroquoian Longhouse Architecture and animated the birthing of the posts from the excavation data map.
This technique which we coined “3D Post Clustering” allowed us to birth poles from site excavation maps automatically. Additionally the technique would grow the height of the pole in relations to the width of the longhouse as indicated in the literature as being equal in length (Bartram, 1751; Dodd, 1984; Kapches, 1994; Snow, 1997; Thwaites, 2008; Wright, 1995). Visually it allows archaeologists to see within 3D space how the poles might have looked and which poles would be associated with each other based on time and space. We immediately saw a need to determine old vs new posts within the archaeological record and whether a technique could be developed to determine which posts were associated with specific longhouse construction and repair periods through the 3D visualization of the data.
Although a simple use of procedural modelling techniques, this process represented the base of future experiments in 3D longhouse construction using archaeological data bringing our research to the next stage, Longhouse 1.5.
Bartram, J. (1751). Observations on the Inhabitants, Climate, Soil, Rivers, Productions, Animals, and Other Matters Worthy of Notice, Made by Mr. John Bartram, in His Travels from Pensilvania to Onodago, Oswego and the Lake Ontario, in Canada. Printed for J. Whiston and B. White, London.
Creese, J. L. (2009). Post-moulds and Preconceptions: New Observations about Iroquoian Longhouse Architecture. Northeast Anthropology 77-78, 47-69.
Dodd, C.F. (1984). Ontario Iroquois Tradition Longhouses. Archaeological Survey of Canada, Mercury Series 124. Ottawa: National Museum of Man.
Kapches, M. (1994). The Iroquoian longhouse architectural and cultural identity. Meaningful Architecture: Social Interpretations of Buildings, 9, 253.
Snow, Dean (1997). The Architecture of Iroquois Longhouses. Northeast Anthropology 53: 61-84.
Thwaites, R. G. (1896-1901). The Jesuit Relations and Allied Documents, 73 Volumes. Burrows, Cleveland, Ohio.
Wright, J.V. (1995). Three dimensional reconstructions of Iroquoian longhouses: A comment. Archaeology of Eastern North America, 9-21.