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An Open Source solution forAn Open Source solution forThreeThree--Dimensional documentation: Dimensional documentation:

archaeological applicationsarchaeological applications

Giulio BigliardiCGT-Centro di GeoTecnologie (Univ. di Siena)[email protected]

Marta BottacchiCGT-Centro di GeoTecnologie (Univ. di Siena)[email protected]

Sara CappelliCGT-Centro di GeoTecnologie (Univ. di Siena)[email protected]

Leonardo CarmignaniDipartimento di Archeologia e Storia delle Arti –Sezione di Preistoria e Protostoria (Univ. di Siena)[email protected]

AIUM 2012 «ARCHEOLOGY INTERNATIONAL UNIVERSITY MEETING» 8th-10th NOVEMBER 2012

Digital models are nowadays present everywhere, their use and diffusion are becoming very popular through the Internet and they can be displayed on low-cost computers.

Although creating a simple 3d model seems to be quite simple, actually the generation of a precise and photo-realistic computer model of a complex object still requires considerable effort.

Three-dimensional digital models are required in many applications such as inspection, navigation, object identification, visualisation and animation.

Recently it has become a very important and fundamental step especially for cultural heritage digital archiving. The aims are different: documentation in case of loss or damage, virtual tourism and museum, education resources, interaction without risk of damage, and so forth.

The specific requirements for many applications, including digital archiving and mapping, involve high geometric accuracy, photo-realism of the results and the modelling of the complete details, as well as the automation, low cost, portability and flexibility of the modelling technique.

Three-dimensional modelling of objects and scenes is an intensive and long-lasting research problem in the graphic, vision and photogrammetric communities.

3D Digital copy can be done by different technology, laser (ground), lidar(aerial), photogrammetry. Lidar

Laser Scanner

Nowadays the most common geomatics techniques used for 3D documentation, reconstruction and interpretation process in the archaeological field are based on image data (e.g. photogrammetry) or range data (e.g. active sensors such as laser scanners). Both approaches have their own advantages and disadvantages and generally the choice between them is made according to the budget, project size and goal, required degree of detail and experience of the working team.

Nowadays 3D scanners are also becoming a standard source for input data in many application areas, but the modern techniques of Structure from Motion (SfM) and Image-Based Modelling (IBM) open new perspectives in the field of archaeological documentation, providing a simple and accurate way in recording three-dimensional data.

In computer graphics and computer vision, Structure from Motion (SfM) and Image-Based Modelling (IBM) methods rely on a set of two-dimensional images of a scene to generate a three-dimensional model.

Compared to laser scanners, the main advantages of SfM and IBM are that the sensors are generally cheaper and portable and that 3D information can be accurately recovered regardless of the size of the object.

Image-Based Modelling and Structure from Motion

The Python Photogrammetry Toolbox(PPT) is an open source system that implements a pipeline to perform 3D reconstruction from a set of pictures.

It takes pictures as input and performs automatically the 3D reconstruction for the images for which 3D registration is possible.

It is done by identifying similar content between N images and solve 3D geometry problems.

User input consist of an image collection and camera parameters. The computed output is a 3D points cloud.

From a set of images…

…to a 3D points cloud

Python Photogrammetry Toolbox (PPT)

Python Photogrammetry Toolbox (PPT) and its Graphical User Interface (PPT-GUI) is developed by Alessandro Bezzi (ArcTeam – Trento, Italy) and Pier Moulon(IMAGINE/LIGM, University Paris Est & Mikros Image).

The suite Python Photogrammetry Toolbox (PPT) is composed of python scripts that automate the different steps of the workflow. The entire process is reduced in two commands: camera calibration and dense 3D points cloud reconstruction.

PPT-GUI is the graphical interface to interact easily with the photogrammetrytoolbox.

The interface is designed in two different parts: a main window composed by numbered panels which allows the user to understand the steps to perform…

…and a terminal window in which the process is running.

RunBundler performs the camera calibration step.

Bundler is a structure-from-motion (SfM) system written in C and C++ for unordered image collections.

It computes the 3D camera pose from a set of images. It needs only two parameters: camera model and sensor width size.

A reduction of the computation time and a decreasing density of the 3D points cloud depend from the setting of the first parameter.

It is a scaling factor of the image size.

Despite the automation, the user can control the final result choosing two initial parameters: the image size and the feature detector.

The final result depend from the setting of the feature detector: PPT can work both with SIFT (patent of the University of British Columbia -freely usable only for research purpose) and with VLFEAT (released under GPL v.2 license). The second one is completely open source, it ensures a more accurate result, but it increases the time of calculation.

Despite the automation, the user can control the final result choosing two initial parameters: the image size and the feature detector.

RunCMVS takes the output of structure-from-motion (SfM) software as input, and perform the dense 3D point cloud computation.

Data conversion from Bundler format to CMVS/PMVS format is made by using Bundle2PMVS and RadialUndistort.

Dense computation is done by PMVS as well as CMVS, that is an optional process that divides the input scene in many smaller instance making the process of dense reconstruction faster.

The main drawback of PPT is that computation speed depends from user's computer. There could be some more drawbacks for large scenes or large images but a compromise between performance and quality can be made by reducing image size with the scaling factor. Generally, the amount of time needed for the processing of image sets is in the order of hours.

• ease of use. The tool should be designed to facilitate users without high 3D modeling skills

• single mesh processing oriented. The system should try to stay focused on mesh processing instead of mesh editing and mesh design where a number of other applications exist a (notably blender, 3D Max, Maya, and many others)

• efficiency. 3D scanning mesh can easily be composed by millions of primitives (points…), so the tool should be able to manage them

MeshLab

The 3D points cloud processed by PPT is displayed and processed in MeshLab. MeshLab is an open source system to create a 3D surface (mesh) from a 3D points cloud (http://meshlab.sourceforge.net/).

MeshLab is developed by Visual Computing Lab of Rome (ISTI-CNR) and is designed with the following primary objectives:

From a set of images…

…to a 3D points cloud withPPT

…to a 3D model withMeshLab.

Points cloud… …mesh…

…3D model with photorealistic texture.

The point cloud can be cleaned of outliers, cropped to the area of interest and then triangulated using one of the merging filters available in MeshLab.

The result is a 3D surface. The colour of the surface (texture) may be derived from the 3D points cloud, which is usually coloured.

Step 1: import the points cloud

Step 2: cleaning the points cloud

Step 3: surface reconstruction (Poisson or Ball Pivoting approach)

Step 4: coloring the mesh using the color of the 3D points cloud

The 3D model

Archaeological applications: Finds

Archaeological finds can be documented “in situ”, taking pictures moving around the object, or in laboratory.

In this case 9 pictures were taken with a NIKON Coolpix L110 at 12 MP.

In PPT the image were elaborated with the feature detector VLFEAT and with a scaling factor of 0.75.

In MeshLab the mesh was created with the Poisson surface reconstruction filter (octree depth 10, solver divide 9) and the coloured.

Archaeological applications: Finds

Archaeological finds can be documented “in situ”, taking pictures moving around the object, or in laboratory.

In this case 9 pictures were taken with a NIKON Coolpix L110 at 12 MP.

The real object

3 cm

The real object

excellent quality for both of details and texture

3 cm

Archaeological applications: Layers

Archaeological field activity is mainly a working process which ends, in most cases, with the complete destruction of the site. Since most of the interpretation is performed in a second stage, it is necessary to collect a massive amount of documentation (images, sketches, notes, measurements).

In the lack of particular expensive equipment (laser scanner, calibrated camera) or software (photogrammetric applications), field documentation is composed by pictures, manual drawings, total station measurements and photo-mosaics. While 3D scanning technologies are able to precisely capture the geometry of the excavation, their cost and operational time discourage an intensive field use.

The alternative is represented by Image-Based Modelling and Structure from Motion technologies, which are able to obtain a 3D model of an element starting from a set of images. Using the same instruments of standard archaeological documentation (digital camera and total station) it is possible to record also the morphology of the level. The data acquisition is fast and simple and it consists exclusively in taking pictures of the area of interest. The same rules of the traditional photography are to be followed: centre the desired object in each picture, avoid extreme contrast shadow/sun, use a tripod in low-light condition. There are no limits about the number of images: but it depends on the complexity of the surface and on the power of the hardware (RAM) which will process the data.

… and many others…

Archaeological applications: Layers - Example 1

In this case 69 pictures were taken with a Sony Cyber-Shot DSC-W90 at 10 MP.




In PPT the image were elaborated with the feature detector VLFEAT and with no scaling factor.

In MeshLab the mesh was created with the Poisson surface reconstruction filter (octree depth 10, solver divide 9).

Isernia La Pineta, Italy (excavation by prof. C. Peretto, University of Ferrara)

Very good quality of the model and of the details …


… and good texture, very photorealistic.





In MeshLab the mesh was created with the Poisson surface reconstruction filter (octree depth 10, solver divide 9)


Not very good quality of the texture: the color is too flat because of poor lighting, but…


… very good quality of the model and of the details.



In this case 227 pictures were taken with a Apple iPhone 4S at 8 MP.



In this case 227 pictures were taken with a Apple iPhone 4S at 8 MP.


In MeshLab the mesh was created with the Poisson surface reconstruction filter (octree depth 10, solver divide 9)


The site


The site

Good result of the details (but centimetric precision,

not millimetric), but not very good quality of the texture because of poor

light

Archaeological applications: UAV Aerial photography

A set of photographs taken with a UAV at different flightaltitude (20 m, 35 m and 50 m) are being developed in collaboration with dr.ssa Paola Piani and Geographikes.r.l. (www.geographike.it)

The 3D points cloud

The 3D model

Conclusions

These experiments demonstrated the possibility to document, in three dimensionsand with a very low budget, archaeological sites and finds.

Advantages:•fast acquisition and processing•good scalability, both small and huge model can be acquired•non-expert users can create his/her 3D model•cheap•it needs only the equipment which is normally used in an excavation (digital camera and total station)•easily portable hardware components allow archaeologists to work under critical or extreme conditions (e.g. in high mountain, underwater or inside a cave)

Disadvantages:•accuracy depends on some factors, in particular the lighting conditions and the performance of the PC used in the processing•the texture obtained by the points cloud is very good for objects of smalldimensions, but less for the large models

On-line resource

References•Gonizzi Barsanti S., Gherdevich D., Degrassi D., 2011. Use Of Low Cost UAV Systems In ArchaeologicalResearch And Disclosure, ISPRS WG V/2 York, UK, Workshop (17 -19 August 2011)

www.academia.edu/1122557/USE_OF_LOW_COST_UAV_SYSTEMS_IN_ARCHEAOLOGICAL_RESEARCH_AND_DISCLOSURE

•Callieri M., Dell'Unto N., Dellepiane M., Scopigno R., Soderberg B., Larsson L. 2011. Documentation and Interpretation of an Archeological Excavation: an Experience with Dense Stereo Reconstruction Tools. VAST11: The 12th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage: 33-40

http://vcg.isti.cnr.it/Publications/2011/CDDSSL11/Callieri_etAl_Documenting.pdf

•Moulon P., Bezzi A., 2011. Python Photogrammetry Toolbox: una soluzione libera per la documentazione tridimensionale, Archeofoss 2011. Open Source, Free Software e Open Format nei processi di ricerca archeologica VI Workshop (Napoli, 9/10 giugno 2011)

http://imagine.enpc.fr/publications/papers/ARCHEOFOSS.pdf (english version)

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