4/8/2023 0 Comments Photogrammetry photoscan![]() Today most stereo photogrammetry packages, at least from my limited experience, allow for the simultaneous calculation of the interior and exterior orientation. Since then the innovations of SfM have been rolled back into the mainstream. At that point SfM was a huge innovation because it didn't required detailed and expensive-to- acquire information about camera position and pose, or special pre-calibrated metric cameras. For a while it could be meaningfully contrasted with the sort of analytical stereo-photogrammetry practiced mainly in aerial mapping applications for decades. The emphasis in SfM was on speed, and not necessarily extremely high accuracy. My understanding is that SfM is an outgrowth of traditional photogrammetry developed by the machine vision community to provide quick 3D data. I'm not sure I would contrast SfM directly with stereo photogrammetry (it is not uncommon in publications to hear of "SfM photogrammetry"). It seems to me that the terminological waters are very muddy here. We prefer the way that PhotoScan pro does this, and we like its workflow for getting this done. The bottom line is that having a very high quality camera calibration, photo pose, and 3D points are critical to getting a high quality result. Once you are ready to create a dense cloud, mesh, and texture, then different algorithms are applied, which use the data you created during alignment, refinement, and optimization. This is in the initial steps of doing the processing. In a stereo pair system, generally there is a separate step to do camera calibration, and then the calibration is locked down and applied to the other processes. In a SfM system all three of the camera calibration, photo pose (alignment, pitch, roll, and yaw) and the 3D points in space influence each other and are adjusted and improved together. As George points out there are other algorithms involved. It is my understanding that Photomodeler uses stereo pairs. With that said, we chose PhotoScam Pro at this time because it is based on the approach of Structure from Motion (SfM) and we also like the feature set. Generally the quality of the photography in the field and the parameters chosen for the dense matching can have such huge impact on the eventual quality of the model that true "apples to apples" comparisons are quite hard to make. There are a few other recent papers that do accuracy testing on a variety of algorithms/software packages, usually against a "ground truth" acquired by some other scanning technique. ![]() Some of the advantages/disadvantages of the two algorithms are demonstrated empirically in this ISPRS paper: Photoscan appears to use the comparatively recent Semi-Global Matching algorithm while I think Photomodeler uses the older Normalized Cross Correlation/Least-squares matching. A good question to ask when comparing photogrammetry packages, particularly with your requirements for high accuracy, is which dense matching algorithm the software uses. I've never worked with Photomodeler personally, but it has been on the market for quite some time.
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