Conservation next point. The sensor records the surface characteristics—cracks, accretions, abrasions, losses—point by point. Software then processes these millions of data points to construct a graphic, three-dimensional representation of the surface. There are several outstanding examples of laserscanning-based technology projects: one is the Digital Michelangelo project, in which a team of 30 faculty, staff, and students from Stanford University and the University of Washington spent the 1998–99 academic year in Italy scanning Michelangelo sculptures and architecture (http://graphics. stanford.edu/projects/mich/). The Stanford/University of Washington group, led by Marc Levoy, used laser triangulation rangefinders, laser time-of-flight rangefinders, digital still cameras, and a suite of software to scan Michelangelo’s statues in Florence,
The art and homes of Georgia O’Keeffe at Ghost Ranch and Abiquiú offer a perfect field environment to test these technologies. notably the David. The scans produced a data point density of one sample per 0.25 mm, detailed enough to see Michelangelo’s chisel marks. These detailed scans produced an enormous amount of data (up to 32 gigabytes); processing the data from those scans is reported to have taken five months. In comparison, the digital technology being explored by the conservation program at the Georgia O’Keeffe Museum utilizes more commonly available, high-resolution digital cameras and carefully engineered but relatively low-tech lighting techniques to gather similar point-cloud data that can then be reconstructed using computer software not
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only to document but comparatively measure detailed physical characteristics related to preservation and condition. High-resolution digital photographic images record detailed, reflected-light, surface information about contour, texture, elevation, and location in two-dimensional space, and gather detailed color data as well. By registering points along the surface of an object and taking multiple images after moving the camera, the object, or the light source, detailed, three-dimensional images can be assembled from two-dimensional digital photographic images. However, perfecting the software and field techniques necessary to capture and measure threedimensional characteristics of objects using digital photography won’t be easy. The point of this promising technology is to be able to automate accurate measurements and monitor small changes in the condition of our collections. The accuracy of the measurement between points in the images is critical and measurement error increases with distance between the camera and the object. Most objects will therefore require multiple photos from small distances and each image will then need to be registered or aligned in order to create accurate three-dimensional models and enable computer comparisons among images taken years apart. We are hoping to fund a two-year, $100,000 case study exploring two theoretical approaches to acquiring digital images that will overcome these obstacles and allow photogrammetric, digital threedimensional reconstructions and automated analyses. If successful, the case study could help spur adoption of these easily accessible technologies for the preservation of art and heritage sites around the world. The first approach is a photometric system developed by Cultural Heritage Imaging, Inc., of San Francisco. Computational photography digitally extracts relevant information from a sequence of digital photographs. This information is then synthesized into a new digital representation, which conveys three-dimensional information about the subject material that is indiscernible in the individual