Mapware can create a 3D digital twin of a landscape from a set of 2D aerial photos. In this article, we discuss the first step in Mapware’s photogrammetry pipeline: keypoint extraction.
When users upload digital images to Mapware and initiate its photogrammetry process, the first step is keypoint extraction: identifying the distinctive features in each image and assigning them values that a computer can easily reference later.
Keypoint extraction begins the photogrammetry pipeline for two reasons.
First, it assists with computer vision, the science of helping a computer understand an image the way humans do by picking out the most interesting shapes from the background. This helps Mapware later in the pipeline when it stitches image sets together into a 3D digital twin.
More importantly, keypoint extraction aids in image compression. Typical photogrammetry projects can require hundreds or even thousands of photos, with each photo containing millions of pixels. Reading these large image sets can be memory-intensive, increasing the risk of system crashes. But keypoints serve as bookmarks in each image file, allowing computers to read the important features and ignore the rest. The result is faster and more-reliable processing.
Mapware identifies keypoints like most photogrammetry software, using a combination of corner detection, descriptor assignment, and invariance calculation.
After Mapware has identified the keypoints in each image and assigned their fingerprints, it can gradually assemble individual images together into the composite that will become a 3D digital twin.
It starts by identifying pairs of images that have nearly identical keypoints. These keypoint pairs exist because drone pilots take photos with overlap to ensure that the same features will appear on more than one image. For example, they may ensure the same feature appearing at the back of one photo appears at the front of the next photo.
If Mapware identifies the same keypoint in both images, it knows to pair them together on their overlapping region. The next article in this series describes the keypoint pairing step, which is called homography.