Photogrammetry Training

photogrammetry training class 2014

CHI offers a 4-day photogrammetry class for groups of up to 15 people. Get practical experience in acquiring photogrammetric image sets and producing scientific 3D documentation with demonstrable accuracy. Learn imaging equipment, capture setups, and software to build 3D content. More…

Calibrated Scale Bars for Photogrammetry

Scale bars placed around mask object

CHI offers a set of 10 calibrated scale bars for photogrammetry. These scale bars are both highly accurate and very efficient for adding scale to 3D imaging projects. More…

See the Photogrammetry Discussion in the CHI Forums

Visit the CHI forums and read about photogrammetry image capture, processing, and software comparisons. More…

CHI's Imaging Project at El Morro National Monument

Petroglyph with sheep in the rock face at El Morro

In June 2015 the CHI team went on location to El Morro in New Mexico and used both RTI and photogrammetry to capture at-risk historical inscriptions and petroglyphs and answer critical questions about them. More…

O'Keeffe Museum: Photogrammetry Videos

After earlier training and consulting with CHI, the staff at the Georgia O'Keeffe Museum in Santa Fe, New Mexico kicked off a summer project in 2012 to document their conservation work using photogrammetry and RTI on objects from the collection as well as two historic houses. More…

Related Publications

BLM Tech Note 248 on Photogrammetry
Sennedjem lintel at the Phoebe A. Hearst Museum


Contents:  What is it?  How does it work?  Example: Tlingit Helmet  How to Capture  Example: Cuneiform Cone Sequence

What is it?

Fundamentally, photogrammetry is about measurement, the measuring of the imaging subject. To perform high-quality photometric measurement, the photographer capturing the photogrammetry data set must follow a rule-based procedure. This procedure will guide the user on how to configure, position, and orient the camera towards the imaging subject in a way that provides the most useful information to the processing software and minimizes the uncertainty in the resulting measurements. These measurements will be as good or as poor as the design of the measurement structure, or lack thereof, that underlies the collection of the photographic data and the means of its subsequent processing.

Recent technological advances in digital cameras, computer processors, and computational techniques, such as sub-pixel image matching, make photogrammetry a portable and powerful technique. It yields extremely dense and precise 3D surface data with a limited number of photos, captured with standard digital photography equipment, in a relatively short period of time. In the last five years, the variety and power of photogrammetry and related processes have increased dramatically.

Video: “Photogrammetry for Rock Art”

Watch this brief video to see an example of a petroglyph rock art panel as a 3D model created using photogrammetry.

Photogrammetry for Rock Art from Cultural Heritage Imaging on Vimeo.

How does it work?

CHI uses an image capture technique based on the work of Neffra Matthews and Tommy Noble at the US Bureau of Land Management (BLM). The BLM Tech Note (PDF) and 2010 VAST tutorial, provide additional information. Neffra and Tommy have been improving their photogrammetry methods at the BLM for over 20 years. Their image capture method acquires photo data sets that are software independent and will get the most information-rich results possible from the various photogrammetry software systems on the market.

Currently CHI uses Agisoft PhotoScan Pro software. PhotoScan uses Structure from Motion (SfM) to simultaneously model the camera’s optical system, the camera’s positions and orientations for each photo, and a sparse cloud of points representing matches of the imaging subject’s features as seen by multiple photos. PhotoScan then uses multi-viewpoint stereo algorithms to build a dense point cloud, which can be transformed into a textured 3D model.

Using SfM algorithms, photographic capture sets can be acquired using uncalibrated camera/lens combinations. To generate the information necessary to characterize how light passes from the imaging subject through the given optical system, SfM algorithms need a set of matched point correspondences. These matched points are found in the overlapping photographs of a planned network of images captured from different positions and orientations relative to the imaging subject. How the camera is moved relative to the subject has a great impact on the degree of uncertainty present in the measurements of the associated 3D representation.

The SfM software must take the information contained in the set of photos and optimally solve for three outcomes:

In SfM, the camera calibration, pose, and 3D point matches are all solved simultaneously. No separate camera calibration is needed or desired. This feature separates SfM from other photogrammetry algorithms, which require either a pre-calibrated camera or an additional set of photos to calculate a calibration for the camera, before point matching commences. The SfM camera calibration is continually improved throughout the optimization operation, as camera pose and matched point uncertainty residuals are systematically reduced.

When these three operations have yielded a very low uncertainty result, expressed in small fractions of pixels, the role of the SfM algorithm is finished. The uncertainty of the solution is quantified in the form of a Root Mean Squares (RMS) residual, which is equivalent to the statistical concept of a standard deviation. This level of uncertainty will serve to characterize all subsequent measurement operations.

The photogrammetry software must then use Multi-Viewpoint Stereo (MVS) algorithms, informed by the knowledge of camera calibration, pose, and a sparse cloud of low uncertainty 3D points, to build a dense point cloud of a size determined by the user. The size of the dense cloud can reach into the hundreds of millions of points.

The photogrammetry software then employs surfacing algorithms, employing the dense cloud’s 3D point positions and the look angles from the photos to the matched points, to build the geometrical mesh. A texture map is calculated from the pixels of the original photos and the knowledge of how those pixels map onto the 3D geometry. The result is a textured 3D model that can be measured with precision.

Example: Tlingit Helmet – Views of a 3D Photogrammetric Model

Tlingit helmet, carved wood

This is a Tlingit helmet made of carved wood by artist Richard Beasley, 1998. Above are three views of a 3D model of it, produced from a photogrammetry image sequence. Top to bottom, left side: detail of input image as object rests on turntable; model in wireframe viewing mode; model in solid viewing mode; model in texture viewing mode. Right side: large background image combines 3 views of the model, illustrating wireframe, solid, and texture.

Adding Measurability

The scale for the project is added during the SfM stage of processing. The scale provides the ability to introduce real-world measurement values to the virtual 3D model. At CHI, we accomplish this by adding at least three (and preferably four) calibrated scale bars of known dimension into the scene containing the imaging subject. The scale bars can be on, around, or next to the region of interest. Each scale bar must be included in multiple (at least three) overlapping images. Scale bars are flat, lightweight linear bars in several sizes with printed targets separated by a known, calibrated distance. The software can recognize the targets. The user then enters distances between the targets. Using calibrated scale bars can produce levels of measurement precision well below one tenth of a millimeter.

Measurement structure design is the process of defining a sensor network and the subsequent methods to process the information it collects. In photogrammetry, the sensor network is the camera’s 3D location and orientation for each photo in the capture set in relation to the imaging subject. To get the best results, this network must collect enough data so that the impact of any incorrect data is minimized. This data set must also enable the reduction to a minimum of the 3D measurement uncertainty of the resulting virtual 3D model representing the imaging subject. The design of the measurement structure is influenced by the imaging subject’s 3D features, any restrictions on the placement of cameras and the number of images necessary to satisfy the given “accuracy” and quality requirements. The prerequisite for any successful measurement in any scientific field of data domain is the design of such a measurement structure. Reduction of measurement uncertainty is accomplished through the reduction and elimination of error in photogrammetric image capture and its subsequent virtual 3D reconstruction.

The final resolution of a dense surface model is governed by the image resolution, or ground sample distance (GSD). The resolution of the images is governed by the size of the real-world area represented by each pixel. The GSD is a function of the resolution of the camera sensor, the focal length of the lens, and the distance from the subject.

How to Capture Photos

A crucial element of a successful photogrammetric process is obtaining a “good” photographic sequence. Good photographic sequences are based on a few simple rules. The CHI photogrammetry training class explores the reasons behind these rules and shows how to make informed choices in the face of challenging subjects.

Here are some suggestions for the camera/lens configuration:

How to determine where to take the photographs:

Archiving the Results

Photogrammetry is archive friendly. Strictly speaking, all of the 3D information required to build a virtual, textured 3D representation is contained in the 2D photos present in a well-designed photogrammetric capture set. Today, the methods of long-term preservation of photographs are well understood. To preserve the textured 3D information of any imaging subject, all that is necessary is to archive the sets of photos and their associated metadata. When a 3D representation is desired, the archived photo sets can be used to generate or re-generate the virtual model. At the current rate of software and computing power development, it is likely that 3D models built from archived photogrammetry image sets will at some point be available “on demand.”

Example: Cuneiform Cone Sequence

The image sequence below shows a 3D model of a section (17mm X 24mm) of a cuneiform cone from the Archaeological Research Collection of the University of Southern California. The sequence is a series of increasing closeups. Each of the images shows the 3D mesh (the underlying geometry) in the upper right, with texture applied in the lower left.

First image of a cuneiform cone from the Archaeological Research Collection of the University of Southern California
  Second and closer image of the cuneiform cone
  Third and closest image of the cuneiform cone