Mesh vertex spacing circa 1cm.

[wptabtitle] Cloud Compare[/wptabtitle] [wptabcontent]

The open source software Cloud Compare, developed by Daniel Girardeau-Montaut, can be used to perform this kind of assessment. The assessment method described here is based on the application of a series of perceptual metrics to 3D models. In this example we compare two 3D models of the same object, one derived from a C10 scanner and one from from a photogrammetric model developed using Agisoft Photoscan.[/wptabcontent]

[wptabtitle] Selecting Test Features[/wptabtitle] [wptabcontent]

Shallow but broad cuttings decorating stones are common features of interest in archaeology. The features here are on the centimetric scale across (in the xy-plane) and on the millimetric scale in depth (z-plane). In this example we assess the resolution at which a characteristic spiral and circles pattern, in this case from the ‘calendar stone’ at Knowth, Ireland is legible, as recorded by a C10 scanner at a nominal 0.5cm point spacing, and by a photogrammetric model built using Agisoft’s photoscan from 16 images.

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[wptabtitle] Perceptual and Saliency Metrics[/wptabtitle] [wptabcontent]

Models from scanning data of photogrammetry can be both large and complex. Even as models grow in size and complexity, people studying them continue to mentally, subconsciously simplify the model by identifying and extracting the important features.

There are a number of measurements of saliency, or visual attractiveness, of a region of a mesh. These metrics generally incorporate both geometric factors and models of low-level human visual attention.

Local roughness mapped on a subsection of the calendar stone at Knowth.

Roughness is a good example of a relatively simple metric which is an important indicator for mesh saliency. Rough areas are often areas with detail, and areas of concentrated high roughness values are often important areas of the mesh in terms of the recognizability of the essential characteristic features. In the image above you can see roughness values mapped onto the decorative carving, with higher roughness values following the edges of carved areas.

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[wptabtitle] Distribution of Roughness Values[/wptabtitle] [wptabcontent]The presence of roughness isn’t enough. The spatial distribution, or the spatial autocorrelation of the values, is also very important. Randomly distributed small areas with high roughness values usually indicate noise in the mesh. Concentrated, or spatially autocorrelated, areas of high and low roughness in a mesh can indicate a clean model with areas of greater detail.

High roughness values combined with low spatial autocorrelation of these values indicates noise in the model.

[wptabtitle] Picking Relevant Kernel Sizes[/wptabtitle] [wptabcontent]

To use the local roughness values and their distribution to understand the scale at which features are recognizable, we run the metric over our mesh at different, relevant, kernel sizes. In this example, the data in the C10 was recorded at a nominal resolution of 5mm. We run the metric with the kernel at 7mm, 5mm, and 3mm.

Local roughness value calculated at kernel size: 7mm.

Local roughness value calculated at kernel size: 5mm.

Local roughness value calculated at kernel size: 3mm.

Visually we can see that the distribution of roughness values becomes more random as we move past the effective resolution of the C10 data: 5mm. At 7mm the feature of interest -the characteristic spiral- is clearly visible. At 5mm it is still recognizable, but a little noisy. At 3mm, the picture is dominated by instrument noise.
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The Gabii Project is an international archaeological project, directed by Nicola Terrenato of the University of Michigan. The Gabii Project began in 2007, seeking to study the ancient Latin city of Gabii through excavation and survey. Gabii, located in central Italy, was a neighbor of and rival to Rome, and flourished during in the first millennium BC.

The excavations at Gabii are uncovering extensive and complex remains within the city’s urban core. Convergent photogrammetry is essential to the project’s recording strategy. At Gabii, this technique is used to document features with complex geometries or large numbers of inclusions, including walls, pavements, rubble collapse, and architectural elements. These types of features can be quite time-consuming to document thoroughly by hand or using conventional surveying in the field. The 3D models collected in the field are georeferenced. They are subsequently simplified for incorporation into the project’s GIS, and compiled into models for distribution online using Unity3D.

You can see a sample model in the Unity3D interface here. You will need to download and install the free Unity webplayer to view the model.

View Gabii Project in a larger map

Detail from the model of the K11 kerbstone, showing decorative carving on the rock surface.

The k15 kerbstone is available here as a sample dataset. You can download the 3D pdf (low res) or the DAE file (high res).

Photogrammetry data from this project was processed using PhotoScan Pro.

Photoscan Pro processing of the model for the K15 kerbstone.

View Knowth TLS and Photogrammetry in a larger map

[wptabs mode=”vertical”] [wptabtitle post=”10743″]Camera Calibration Documentation[/wptabtitle] [wptabtitle post=”10734″]Image Acquisition Documentation[/wptabtitle] [wptabtitle post=”10729″]Control Point Documentation[/wptabtitle] [wptabtitle post=”10749″]Image Processing Documentation[/wptabtitle] [wptabtitle post=”10754″]Deliverables Documentation[/wptabtitle] [wptabtitle post=”10720″]Project Level Documentation[/wptabtitle] [/wptabs]

[wptabtitle] Demo files[/wptabtitle] [wptabcontent]The web viewing script is here as a zip file. You will need a functional web server to place the files in. The script loads the model through an XMLHttpRequest, which requires the web page be loaded from an http server. The model won’t load if the web page is opened from the hard drive. [/wptabcontent]

[wptabtitle] OpenCTM and Meshlab[/wptabtitle] [wptabcontent]The easiest way to convert a model to OpenCTM is to use the open source 3D modelling program MeshLab. Once you have imported the mode into MeshLab, we will perform a number of steps to prepare the model for web delivery. [/wptabcontent]

[wptabtitle] Texture to Vertex Color[/wptabtitle] [wptabcontent]
1.The current JavaScript doesn’t support textures, but uses vertex color instead. To convert textures to vertex color, in MeshLab go to Filter > Texture to Vertex Color (between 2 meshes)
2.Make sure the Source Mesh and Target Mesh are the same. Then press Apply.
3.Toggle the textures off to check if the conversion was successful. Render > Render Mode > Texture. You can also find the icon in the main toolbar.
4.If the texture still appears to be shown on the model after toggling the textures off, you know that the Texture to Vertex color was successful.

meshlab texture icon

1. Filters > Cleaning and Repairing > Close Merged Vertices

2. Filters > Cleaning and Repairing > Remove Duplicated Face

3. Filters > Cleaning and Repairing > Remove Duplicated Vertices

4. Remove Face from Non Manif Edges

These steps are the preliminary cleaning methods before export. After you complete these steps, export the mesh and try to load it in the browser. If the model loads, all is well. If the browser hangs up or crashes, we’ll have to perform additional cleaning steps
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[wptabtitle] Manual Cleaning[/wptabtitle] [wptabcontent]For manual cleaning, we need to cut any section of the mesh that appears questionable. Typically this will be near and on the edges of the mesh. The screenshot below gives a good impression of what to look for.

1. Rotate the model so that the section in question is horizontal.

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[wptabtitle] Manual Cleaning 2[/wptabtitle] [wptabcontent]
2. Click Edit > Select Faces in a Rectangular Region
3. Drag and select the area in question

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[wptabtitle] Manual Cleaning 3[/wptabtitle] [wptabcontent]4. Click Filter > Selection > Delete Selected Face and Vertices

5. Repeat for other questionable sections of the mesh
6. Export
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[wptabtitle] Exporting[/wptabtitle] [wptabcontent]Whenever you want to give the model a test run, you’ll export it out as a OpenCtm file.

1. Go to File > Export Mesh as
2. In the Files of type below, select “OpenCTM compressed file (*.ctm)” from the dropdown men
If OpenCTM is not an available option for export, you may need a more recent version of Meshlab
3. Save as…

Be sure you save the model on the web server where the javascript file is located.
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[wptabtitle] View Your Results[/wptabtitle] [wptabcontent]To see the model in the web viewer, we’ll need to open up the demo.html file and change one line of code.

1. Open the demo.html file in your favorite editor.

2. On line 72, you will find in quotes the string “changeME.ctm”

3. Change this to the name of the model. If you named the model “myCoolModel.ctm”, you want to add “myCoolModel.ctm” with quotes. If you placed the model in a folder, be sure to add the directory as well, like this “/myModels/myCoolModel.ctm.”

4. Open the website. If you placed it on a local webserver, the address will be something like “http://localhost/demo.html.” if you placed the web scripts and model on a different server, use the name of the server.

NB: Once you have exported as OpenCTM, you can also export out as OBJ for a Unity model as well.
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[wptabs type=”tabs” effect=”none” mode=”vertical”] [wptabtitle] Learn more about depth of field, bit depth, EXIF metadata, file formats, and more…[/wptabtitle] [wptabcontent]
Glossary of terms found at Digital Photography Review website (dpreview.com)
http://www.dpreview.com/learn/?/Glossary/[/wptabcontent] [wptabtitle] ADS Guides to Good Practice[/wptabtitle] [wptabcontent]

Learn more about the ADS Guides to Good Practice for close-range photogrammetric documentation…
Barnes, A; J. Cothren, K. Niven (2011) Guides to Good Practice: Close-Range Photogrammetry. Archaeology Data Service / Digital Antiquity: Guides to Good Practice.
http://guides.archaeologydataservice.ac.uk/g2gp/Photogram_Toc[/wptabcontent]

[wptabtitle] Documenting Surfaces[/wptabtitle] [wptabcontent]An excellent guide for using close-range or aerial photogrammetry to document surfaces (e.g. rock art, trackways, surfaces prone to erosion).
Matthews, N. A. (2008) Aerial and Close Range Photogrammetric Technology: Providing Resource Documentation, Interpretation, and Preservation. Technical Note 428. National Operations Center, Denver, Colorado: U.S. Department of the Interior, Bureau of Land Management.
http://www.blm.gov/nstc/library/pdf/TN428.pdf[/wptabcontent]

[wptabtitle] HABS/HAER/HALS[/wptabtitle] [wptabcontent]Learn more about architectural and engineering documentation for long term archival in the Heritage Documentation Programs collections in the Library of Congress.
National Park Services: Heritage Documentation Programs (HABS/HAER/HALS)
http://www.nps.gov/history/hdp/[/wptabcontent]

[wptabtitle] Cultural Heritage Site Surveying[/wptabtitle] [wptabcontent]What makes a successful and useful survey of cultural heritage sites? Learn what to record, how to record it, and what performance indicators are important…
Bryan, P., B. Blake, J. Bedford, D. Barber, J. Mills, and D. Andrews (2009) Metric Survey Specifications for Cultural Heritage. English Heritage, Swindon.
http://www.english-heritage.org.uk/publications/metric-survey-specification/[/wptabcontent]

[wptabtitle] English Heritage on Photogrammetric Survey of Historic Buildings[/wptabtitle] [wptabcontent]An extensive and detailed guide for the photogrammetric survey of historic buildings…
D’Ayala, D., and P. Smars (2003) Minimum requirements for metric use of non-metric photographic documentation, University of Bath.
PART 1: http://www.english-heritage.org.uk/publications/metric-use-of-non-metric-photographic-documentation/metricextraction1.pdf/
PART 2: http://www.english-heritage.org.uk/content/publications/publicationsNew/metric-use-of-non-metric-photographic-documentation/metricextraction2.pdf[/wptabcontent]

[wptabtitle] Intro to Architectural Photogrammetry[/wptabtitle] [wptabcontent]A nice introduction to close-range photogrammetry and methods for architectural photogrammetry…
Hanke, K., and P. Grussenmeyer (2002) Architectural Photogrammetry: Basic Theory, Procedures, Tools. September 2002, ISPRS Commission 5 Tutorial, Corfu.
http://www.isprs.org/commission5/tutorial02/gruss/tut_gruss.pdf[/wptabcontent] [/wptabs]

3D View of DEM Overlaid With 3D Breaklines

Close Range Photogrammetry:

Typical deliverables created as the end result of a CRP project could include 2D vector graphics (planimetric or elevation type CAD drawings), dense point clouds, 3D polylines, facetized models (mesh) of an object or surface, and raster graphics such as rectified or fully orthorectified images. Each deliverable created should include appropriate metadata for each of the above mentioned steps, as well as metadata for the additional processing performed to create the final file.

Because the diversity of possible end products and software packages associated with photogrammetry, it is difficult to specify the metadata required. We recommend using the ADS Guides to Good Practice to identify the appropriate metadata for your specific file. Below is a list of ADS Guides and the file types they should be used for:

2D CAD drawings and 3D vectors: http://guides.archaeologydataservice.ac.uk/g2gp/Cad_Toc
3D point clouds and facetized models (mesh): http://guides.archaeologydataservice.ac.uk/g2gp/LaserScan_Toc
Raster images: http://guides.archaeologydataservice.ac.uk/g2gp/RasterImg_Toc

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Low Altitude & Aerial Photogrammetry:

A discussion and comparison of file export formats is currently being developed. Please check back soon! And in the meantime, please see the ADS Guides to Good Practice regarding Aerial Survey for detailed information regarding documentation and standards.

Aligned Images in PhotoScan

In order to extract three dimensional points from two dimensional images, it is necessary to perform a triangulation with at least two images (a stereo pair). When more than two images are used in a triangulation, we refer to the group of images as a ‘block’. To perform a triangulation, we must measure a sufficient number of tie, control, and/or check points throughout the block. Constraints may also be placed on certain sets of points to enforce angular, linear, and/or planar properties. Once a triangulation is successful, image exterior orientation parameters (along with estimate for accuracy) should be available to the user. These are important pieces of information for downstream deliverables and should be documented. The table below describes the appropriate documentation for this process. Download a printable form in PDF format here or in a spreadsheet (.xlsx) format here.

Camera Calibration Photo

Each camera (referring to a camera and lens combination) used in a close-range photogrammetry project should be calibrated and a calibration file should be saved with the project data. Some close-range photogrammetry software does not require this information before processing the image block; however, it will provide most of this information after processing. The tables below describe what is appropriate to document for this process. Download a printable form in PDF format here or in a spreadsheet (.xlsx) format here.

For each camera/lens calibration:

For each calibration image (if performing a camera calibration with a target):

Nikon D70

Organization and documentation during image collection in the field is especially important. The table below describes the appropriate documentation for this process. Download a printable form in PDF format here or in a spreadsheet (.xlsx) format here.

For each group of images: