Seeing into the Past: Creating a 3D Modeling Pipeline for Archaeological
Visualization
P. Allen
S. Feiner
A. Troccoli
H. Benko
E. Ishak
B. Smith
Department of Computer Science, Columbia University, New York, NY
Abstract
Archaeology is a destructive process in which accurate
and detailed recording of a site is imperative. As a site is
exposed, documentation is required in order to recreate andunderstand the site in context. We have developed a 3Dmodeling pipeline that can assist archaeologists in the documentation effort by building rich, geometrically and photometrically accurate 3D models of the site. The modeling effort begins with data acquisition (images, range scans, GIS
data, and video) and ends with the use of a sophisticatedvisualization tool that can be used by researchers to explore
and understand the site. The pipeline includes new methods for shadow-based registration of 2D images and temporal change detection. Our multimodal augmented reality system allows users wearing head-tracked, see-through,head-worn displays to visualize the site model and associated archaeological artifacts, and to interact with themusing speech and gesture.

1. Introduction
Archaeology is a destructive process. Analysis of a siterequires the dismantling of structures and the displacement
and removal of findings, such as tools, pottery and bones.
Therefore, it is important to keep an accurate record of eachsite as it is being excavated. Our work focuses on developing new tools and methods to model and visualize historicand archaeological sites.
In this paper we address several problems. The first is
how to digitally record the progress of an ongoing archaeological excavation using different information sources: 3D
laser scans, digital video, panoramic photography and GIS(geographic information systems) data. Our technical goal
is to complement existing field methods with these technologies, as shown in Figure 1. Building 3D models fromrange scans and images is time-consuming and difficult,usually involving much manual effort. In a typical 3D modeling pipeline, geometry is acquired in the form of range
scans that need to be registered together in a common coordinate system. Additionally, images provide texture information. To build a texture-mapped model, the images

Figure 1. Members of our team record exact 3D coordinates using a total station surveying instrument (upper left), capture videoof an excavation using a scanner and digital camera (lower left),and scan a portion of the excavation site using a time-of-flightlaser range scanner (right).

and the geometry have to be registered (i.e., a mappingfrom model space to image space has to computed). Partof our research focuses on developing new methods for reducing model-building time and improving accuracy [2]. In
particular, texture mapping can pose difficult problems inproviding complete and accurate coverage of a complicatedmodel. Our method uses knowledge of shadows and sun position during the image acquisition process to correctly register the imagery with the constructed 3D model. But ourinterest goes beyond building a 3D model; we also want
to record and keep track of the changes to the site as the
excavation proceeds. This problem also poses interesting
technical challenges. Scanning the entire site every day is
not desirable or practical. Instead we would like to acquireenough information to track changes, so as to be able tobuild a 3D model that represents a snapshot of the site at
any given point in time.
A second problem we address in this paper is the development of a visualization tool that will enhance the postexcavation analysis and review process. For this purpose,
we have built a multimodal augmented reality system for

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Figure 2. Our 3D modeling and visualization pipeline. We start by building a textured 3D model using range scans and images, which
we enhance with contextual information in the form of panoramic images, video, and GIS data. This context-rich model is then used as
input to our multimodal augmented reality application.

viewing the 3D model and the accompanying contextual information. In our prototype, users wearing head-tracked,
see-through, head-worn displays can explore a full-size representation of a virtual site, and inquire about findings andobjects using both gesture and speech. Additional information in the form of still images, video and textual description is also available. Figure 2 describes our modeling andvisualization pipeline. Each of the steps in the pipeline is
described in the sections that follow.

2. Previous Work
3D modeling. Over the past few years, a number of research teams have been addressing the use of range scans
and images to develop 3D models for the virtual preservation of historic sites. Some notable projects include themodeling of Michelangelo’s David and other statues by
Levoy et al. [10], the IBM Pieta project of Bernardini etal. [4], the Great Buddha project of Ikeuchi et al. [8], andthe virtualization of a Byzantine Crypt by Beraldin et al.
[3]. Our goals overlap with the work of these researchers,
but also differ in several ways. First, we are interested inrecording an archaeological excavation in progress. Second, we want to keep track of changes as the excavation
proceeds. And finally, we require that our models serve as
a complement to the archaeologists’ documentation, whichmeans sharing the same reference coordinate system. The
work of Acevedo et al. [1] is similar to ours with respect
to these latter goals. However, they use photogrammetry,instead of range data from laser scans, to create their 3Dmodel; hence, the technical challenges we each face arequite different. Image-based reconstruction for archaeology
has also been addressed by Pollefeys et al. [12].
Visualization. Archaeologists currently use many kindsof written documentation, sketches, diagrams, and photographs to document the physical state of an excavation
site, following standard guidelines that have been developed for this purpose. To visualize the data, most archaeologists currently rely on GIS software, such as ESRI’s
ArcGIS. Recently, INTRASIS has extended some capabilities of standard GIS systems and functions as a plug-in for
ArcGIS. Standard computer-aided design (CAD) systems,
such as AutoCAD, are often used for modeling and reconstruction, which is both costly and time-consuming. Whileboth GIS and CAD contain 3D visualization capabilities,
most of those systems tend to present layered 2D maps orcoarse topographical terrain maps with embedded objects,
sketches, and pictures. They do not support additional multimedia, such as audio, video, 3D high-resolution terrainscans and panoramas.
Several research groups have explored immersive 3D visualization for archaeology. For example, the ARCHAVE
project [1] was developed for use in a CAVE-like environment with projected walls and floor [5]. It consists of ahuman-modeled environment embedded with virtual icons
representing various types of finds and has been used to determine patterns and trends of the objects found on site.
In contrast, our system uses a highly detailed computer-

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generated model, containing high-resolution textures, making possible a more accurate representation of the actualsite. Gaitatzes et al. [7] present virtual reality environments for visualizing temples and public buildings of ancient Greece. Relatively stationary users visualize an archaeological site on a tilted rear-projected desk or in a
CAVE-like room. In contrast, users of our system can physically walk around the life-sized excavation site to explore
the virtual world from different perspectives.
In the following sections, we present the 3D modelingpipeline and visualization facilities that we have developed.
We illustrate them with examples based on data that we acquired on-site at the excavation of a 6th-4th century BC archaeological excavation at Monte Polizzo in western Sicily.

3. The 3D Modeling Pipeline
Geometry, in the form of point clouds, is acquired by alaser range finder, and texture, in the form of photographs,
is obtained with a digital camera. Details for each stage are
given below. A video of our model can be downloaded fromwww.cs.columbia.edu/∼allen/sicily.avi
Scan acquisition. To model the acropolis at Monte
Polizzo we used a time-of-flight laser scanner (Cyrax 2500)
to measure the distance to points on the site. Data from thescanner comprises point clouds, with each point consistingof three coordinates (x,y,z) and a value representing the amplitude of the laser light reflected back to the scanner.
Scan registration. Multiple scans are required to completely acquire a site such as the Monte Polizzo acropolis.
The resulting point clouds need to be registered together.
Typically, the coordinate system of one of the point clouds
is chosen as the coordinate system for the model. In archaeology, however, a global site coordinate system is set
up from GPS data. A set of control points are accurately
measured using (preferably differential) GPS, and are thenused to initialize a total station (e.g., a Leica TCR 705 total
station, as shown in the top left portion of Figure 1) in theworld coordinate system. A total station is a theodolite with
an electronic distance meter that is used to measure points
of interest, such as the location of findings, rocks, or the
terrain contour. To register each point cloud with respect
to the site’s coordinate system. we use a set of targets thatthe scanner can automatically recognize, shown in Figure 3.
Before taking a scan, we place the targets on the area weplan to cover, and use the total station to measure their positions in the site’s coordinate system. Afterwards, we scan
the scene at a low resolution to identify and acquire the targets’ positions in the scanner’s coordinate system, and sosolve the 3D-to-3D registration problem. The targets arethen removed and a full-resolution scan is acquired.
This technique allowed us to accurately register eachindividual point cloud with the site’s coordinate system.

Figure 3. Modeling the acropolis at Monte Polizzo. Top: Targets
have been placed in the area to be scanned. Bottom: Final textured
model with panoramic image as background and GIS data.

For our purposes, it proved advantageous over pairwise or
multi-scan registration using the iterative closest point algorithm (ICP) for several reasons. First of all, it required
no scan overlap. This allowed us to take fewer scans, withgreater freedom to choose the scanner position by eliminating the traditional overlap requirement of ICP. We did,
however, acquire overlapping scans to minimize unseen regions (holes). In addition, as soon as we finished a scanningday, our model was completely registered. And finally, it allowed us to record changes easily by scanning only the affected areas. We would not have been able to track changes
robustly if we had relied on ICP for scan registration, because ICP relies on point-to-point correspondences over anoverlapping set of point clouds. If the point clouds represent different states of the site, these correspondences may
not exist.
Surface generation. From sets of registered point
clouds that represent the state of the site at the same point
in time, we generated a triangular-mesh surface, using the
VripPack software developed by Curless and Levoy [6].
VripPack outputs the best mesh that fits the point cloud data,

Proceedings of the 2nd International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT’04)
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3.1. Texture mapping
Texture acquisition. In addition to the scanner, we useda Nikon D100 digital camera, mounted on the scanner’scase, to acquire texture information. For each scan we acquired, we took a photograph.
Local texture registration. Prior to our trip, we performed a simple calibration to estimate the camera’s external and internal parameters. We determined the camera calibration by scanning a flat wall with the room lights off andthe camera’s shutter open for the eight-second duration of
the scan. This provided us with an image of the grid pattern described by the laser as it sampled the wall and the 3D
coordinates of each sample. We scanned again from a different distance and angle to acquire more samples. We thensegmented the images to obtain the centroid of each grid
point, and solved the calibration problem using the 2D-to3D correspondences just obtained.
Global texture registration. While the local texture
calibration procedure provided us with a good estimate of
the camera’s parameters, we found that our images wereslightly misaligned with respect to the complete model. One
reason for this is that our calibration was local to the scanner’s coordinate system. To texture-map the final model,
this local registration had to be transformed to the site’scoordinates. Hence, any errors in scan-to-scan registration
will also affect the texture registration. In addition, our initial calibration was accurate at the depths at which calibration points had been measured, but not as accurate at otherranges. To solve these misalignments, we developed a new
method based on the shadows cast by the sun. Our method
performs a global texture registration; it registers the texture
with respect to the model’s coordinate system, as opposed tothe scanner’s coordinate system. Since we have the latitude
and longitude of the site and the time at which each photograph was taken, we can compute the location of the sun
and find portions of the 3D model that should be in shadow.
By matching these with the shadows in the image we solvethe 2D to 3D registration problem.
Assuming the internal parameters of a camera are
known, we find the camera position c with respect to the 3Dmodel: c = (φx , φy , φz , tx , ty , tz ). This is a six-parameter
rigid body transform that maps a point Xw in world coordinates into its corresponding point Xc in the camera reference frame. The first three parameters (Euler angles)
represent the angles of rotation about each of the coordinate axes and form a rotation matrix, R(φx , φy , φz ) =Rx (φx )Ry (φy )Rz (φz ). The remaining three parameters
are the components of a translation vector t. Together, theysatisfy the following relationship:
Xc = R(φx , φy , φz )Xw + t.

If we knew the correct set of external camera parameters(φxf , φyf , φzf , txf , tyf , tzf ), then an orthographic view ofa textured version of the model with the eye looking in the
direction of the sun’s rays should show no texture representing shadows. However, if the texture is misaligned, sucha rendering will exhibit a number of shadow pixels. Our
method exploits this idea by searching the parameter space
for a point that minimizes the number of pixels representing
shadows in the rendered image of the model.
The problem is properly stated as follows. If we let I
denote the image to be registered and M the model, then f ,our cost function, is defined asf (Ir ) =

shadow(Ir , x, y),x,y∈Irwhere Ir stands for a rendered image of M as seen from thedirection of the sun and textured with I using a texture camera with external parameters set to c, and shadow(Ir , x, y)
is 1 if pixel(x,y) of Ir is in shadow, otherwise 0.
Given the initial estimate of the camera position c0 that
we obtained off-line with our pre-calibration, the problem
is to find a point cf that minimizes f .
The complete shadow registration process consists of
two stages: a preprocessing stage and a minimization stage.
In the preprocessing stage, the shadows in the image arefound using thresholding, and masked out with a given
color. In the minimization stage, simulated annealing isused to search for a global minimum of f , starting fromthe initial estimate. We applied the algorithm to ten of the
texture images we used to create our model. Figure 9 shows
screenshots of the final textured model. A detailed analysis
and quantitative results can be found in [14].
Texture-map generation. To create the final texturemapped model, we assigned each mesh triangle one of theavailable textures. For each vertex v, we first find its valid
image set Iv . An image ik belongs to the valid image set ofa vertex if the following three conditions are met: first, theprojection of v must be within the boundaries of ik ; second,
the normal nv of v must be pointing towards the camera of
ik (i.e., the angle between the optical axis of of the camera
and nv must be less tan π/2); and, finally, there must be noother point in the mesh M in the line connecting the projection of v in ik and v (these conditions are mentioned in[13]). We perform the last test using a ray-tracing operation accelerated with an octree. We then compute for every
triangle t its valid image set It . An image ik is in It if it
belongs to the valid image set of each of the triangle’s vertices. Finally, from the valid image set of each triangle, wechoose the image ik with the best pixel/area resolution. The
final textured model is rendered using hardware-supported
projective texture mapping.

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Figure 5. Tracking changes. Left: Initial model. The stones to be removed are numbered. Middle: Model after stones have been
removed. Right: Detected changes, in light gray and numbered. The careful observer will notice some small gray patches that do not
represent removed stones. These are due to small misalignments and smoothing errors.

of the structure by registering and merging the initial point
clouds together. We then incrementally incorporated thechanges to the model. From the initial model M0 and apoint cloud P that was acquired after the stones were removed, we detect the geometry corresponding to the removed stones, delete it, and add the geometry corresponding to the newly exposed surfaces to obtain a new instanceM1 of the model, in the following manner:
1. Let Xp be the position of the scanner when P was
taken.
2. For every point pi in P do
Figure 4. Incremental changes in the model are detected by casting a ray from the scanner position to the model through point pi ,and finding its intersection point mi .

(a) Trace a ray R from Xp through pi (see Figure 4).
(b) Find the triangle ti of M0 that R intersects.
(c) Find mi , the intersection point of R and ti .
(d) Compute the distance di between pi and mi .

3.2. Model change detection
We have also developed a method to track changes tothe excavation site. Because of the large size of the Monte
Polizzo site, we limited ourselves to a single structure, approximately 10 by 10 meters in area. We first acquired thescans necessary to build a full initial model of the structure. The archaeologists then proceeded to remove somestones and we acquired new scans. We did not scan theentire structure again; instead we scanned only those areas
where stones had been removed. Using our change tracking method, we can recreate the site with and without theremoved stones, simulating the archaeological process itself. Figure 5 shows renderings of the site model at different stages, where the numbers labels the stones that were
removed. The first image shows the site in its initial state,
the middle one shows the site after the stones were removed,
and the right image shows the detected changes in gray.
During post-processing, we created an initial model M0(e) If di is greater than a given threshold, and R
passes first through mi and then through pi , remove all edges of triangle ti from M0 .
3. Remove all non-connected vertices from M0 . Call thisthe intermediate model Mim .
4. Create a mesh Mp from P and use a package such as
VripPack to merge Mim with Mp to obtain M1 .
These steps are repeated for all point clouds acquired after the stones were removed. The result is a new model that
represents the updated state of the site.

3.3. Adding context information
3D modeling from range scans is a powerful tool, but a
3D model by itself lacks important context information. We
can obtain this context by combining data from different
sensors. In our pipeline, we combine our 3D model withsurveying data, panoramic images, and digital video.

Proceedings of the 2nd International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT’04)
0-7695-2223-8/04 $ 20.00 IEEEIn current archaeological recording methods, a total station is used to lay out a site grid and record the 3D positionof finds. The logged data is then used to make a site plan,
typically using GIS software. We can incorporate this datain our modeling pipeline to add important and meaningful
information. For example, by displaying the site grid, archaeologists that were not at the site can relate written reports to the 3D model. The GIS data and the 3D model are,by design, in the same coordinate system and no special
registration is required.
Additional context information is provided by panoramic
images. In the field, we acquired a complete panorama froma set of thirty eight images using a Kaiden QuickPan III
spherical panoramic head. The final cylindrical panorama
was created with PhotoStitch, a photo-stitching package.
For registration purposes, we recorded the position of thecamera using the total station. (We did not record the camera’s orientation, which we had to find manually.) Since thecamera was leveled, finding the correct rotation is a one–degree-of-freedom search (rotation about the camera’s yaxis), which we performed manually by rotating a texturedmapped cylinder around the model.
Finally, we used a color digital video camera to capture
moving imagery of the excavation in progress, recording theposition of the video camera with the total station.

Figure 6. A user, wearing a head-tracked, see-throughhead-worn display, an instrumented glove, and a hand
tracker.

4. Visualization for Archaeology
Archaeological excavations are projects that involve
many experts and usually last for years. Most of that timeis actually spent away from the excavation site in preparation for each excavation season, or in analysis of data thathas been excavated during the previous seasons. In addition, as the site is excavated, the layers are removed without
ever being able to physically recreate them, and most of the
excavated material is stored in a museum repository that is
not easily accessible for analysis. Our system is intended
to explore how these problems might be solved. By digitally recording the excavation, we allow archaeologists toremotely visualize the site at will. We combine a 3D layered
terrain model of the site, with models of excavated objects
that are correctly located in the site model.
In our visualization environment, shown in Figure 8,
multiple users, each wearing a head-tracked, see-through,head-worn display (Figure 6), can simultaneously examine
the site model. We believe that this approach has several
benefits compared to CAVEs [5] and similar environments,
in which one or more users occupy a room whose walls(and, possibly floor and ceiling) are projected with imagery
computed for the perspective of a single tracked user:• Multiple users can each have a correct 3D stereo viewof the same or a different part of the the environment,
depending on the user’s location.

Figure 7. Live video shot showing the visual representation offour SenseShapes that are used in our system; two are attachedto the user’s head (representing the user’s view frustum) and theother two to the hand (a pointing cone and a grabbing sphere).

Figure 8. Our augmented reality visualization system. A user in
our lab explores a full-size model of the site and selects one of thefindings to examine in detail. This image is taken through a live,
tracked, video see-through display.

Proceedings of the 2nd International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT’04)
0-7695-2223-8/04 $ 20.00 IEEE• The active area can be large enough (limited only by
the tracking technology), that the excavation site canbe explored at full size, as users walk around it.
• See-through displays allow users to see each other andinteract without the need of virtual avatars.
• While head-worn displays of the sort we currently use
have a lower pixel count (800×600) than one wallof a typical current projection system (1024×768 or
1280×1024), the otherwise disadvantageous smaller
field of view of the head-worn display means that eachpixel subtends a smaller visual angle.
Our visualization environment is intended for many applications, including post-excavation analysis, educating archaeology students about excavation procedures, and training new personnel prior to the excavation season.
Each user is able to view a 3D representation of theterrain. In addition, small archaeological finds are placedaround the site at the exact locations of their discovery, eachlabeled with its name and description, as shown in Figure 8.
A 360-degree panorama surrounds the model to provide an
immersive virtual experience. As a user walks around thevirtual site, she can inquire about situated objects througha combination of speech and gesture, or navigate using awireless mouse. Additional information about specific objects is available in the form of still images, movies, andtextual descriptions that can be presented on the head-worndisplay.
Digital images, taken on site, make it possible for archaeologists to visualize the site’s overall structure, as wellas the geometry and texture of individual stones, both thoseremoved during the excavation and those still in place.
Movies allow archaeologists to witness the excavation process. They also provide an invaluable walkthrough perspective of the site, making it possible for archaeologists to experience the excavation both visually and audibly throughthe eyes of the camera-person. Archaeologists also have access to a textual database of information about the various
trenches, objects, and excavations on site. This information
is available both in a tabular fashion in a fixed portion of thedisplay, as well as in situ, labeling the objects found in the
3D environment.
Our environment uses a multimodal interaction engine
that combines speech input with glove-based gestures to facilitate easy and natural object selection. We are using IBM
ViaVoice V.10 for speech recognition and EssentialReality P5 gloves with our own gesture recognizer. Currently
we can differentiate between three gestures (point, grab,
thumbs up) and an idle state. To help disambiguate references to closely located objects, we have used SenseShapes
as a primary mechanism for interacting and selecting the objects in the environment [11, 9]. SenseShapes (Figure 7) arevolumetric regions of interest that are attached to parts of auser’s body (e.g., the head and a hand) to provide statisticalinformation about the user’s interaction with objects. The
statistics describe, for any selected time interval, how longan object has intersected a SenseShape’s volume, the number of times the object has entered and exited the volume,the distance of the object from the volume’s origin, howmuch of the object is visible from a key point within thevolume, and how close the object is to the volume’s center
line. The system keeps a complete history of object intersections with the SenseShapes in an in-memory database,thus making it possible to aggregate the statistics over time.
This effectively allows the system to formulate higher levelqueries that express requests such as “Return all objects oftype U and statistics associated with them that were in thehand-picking cone and in the user’s view cones that happen
to have intersected the hand-picking cone between time V
and time W.” The returned object list is then sorted based ona weighted average of the object statistics. Furthermore, by
combining this hypothesis list with the output of the speech
recognizer and the gesture recognizer, the system can makefairly sophisticated multimodal decisions.

5. Summary and Conclusions
We have described an integrated 3D modeling and visualization pipeline for archaeology, which we have applied
to digitally recording an excavation in progress at Monte
Polizzo, Sicily. The area of 3D modeling for cultural heritage preservation is evolving rapidly and we believe thatnew tools such as these will be an important resource for
future archaeological research. Our work is unique in a
number of ways, because it 1) integrates a variety of different data sources: range scans, images, GIS data, andvideo, 2) develops a new technique for image–to–modelbase registration based on the shadows cast by the sun,3) includes a temporal change racking component, and 4)
presents a novel set of multimodal augmented reality visualization tools for archaeology that can be used with thisand other models.

Acknowledgments
Lynn Meskell and James Conlon assisted with the field workat Monte Polizzo. Ian Morris and the Stanford Archeology Center
graciously provided access to and images and data from Monte
Polizzo. Sajid Sadi and Avanindra Utukuri provided support with
the Essential Reality P5 glove. This research was funded in part by
ONR contracts N00014-99-1-0394, N00014-99-1-0683, N0001404-1-0005, and N00014-99-1-0249, NSF grants IIS-0121239 and
IIS-00-82961, and gifts from Mitsubishi Electric Research Labs,
Microsoft Research, and Alias Systems.

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Figure 9. Screenshots of our 3D model with the panorama in the background.

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