Army photogrammetry technique makes 3D aerial maps in minutes
techcrunch.com
techcrunch.com
"Method 100 automatically processes FMV data for delivering rapid two-dimensional (2-D) and three-dimensional (3-D) geospatial outputs." According to [0041] 'Method 100' is FIG 1? Looking at FIG 1, it talks a lot about clusters and metadata. From [0026] and [0039] and [0064] it seems like a cluster might be a collection of images containing a point of interest. [0089] says they do measure the altitude of the aircraft. Since they have the metadata of camera FOV they can place each image in a 3D world. I think they take a lot of redundant images and they have many views of each object of interest, which is enough to give them a (x,y,z) estimate for their points of interest.
"The increased processing speed of the system is achieved by configuration of system components to perform novel geometric calculations rather than by increased processing capability."
This gives me the idea that there is a ridiculous amount of data per scene.
I suppose when you control your own data collection, the methods can be completely different than what is worked on in academia. Compare with single image depth estimates, or even for depth maps from video for self driving.
I guess it's good that deep learning isn't being used here. Now that I think of it, it is not appropriate.
I like this part lol: "Method 100 further may be automated using various programming scripts and tools such as a novel Python script generator."
He had a publication on there belonging to "9th NATO Military Sensing Symposium, 2017" from which papers/presentations are made public: [1], surprising! But I didn't find Massaro's paper there. I also searched for his "Videogrammetric Mapping from Unmanned Aerial Systems" but did not find it in ISPRS or elsewhere. A commercially available software that seems to use a similar method: [2]
The Techcrunch article is likely a derivative of the earlier Techlink article: [3]
[0]: http://mason.gmu.edu/~rmassaro/CV_Massaro_complete_20180816.... [1]: https://www.sto.nato.int/publications/STO%20Meeting%20Procee... [2]: https://www.pix4d.com/blog/underwater-mapping-videogrammetry [3]: https://techlinkcenter.org/new-us-army-software-rapidly-conv...
Photogrammetry apps with a good, automated drone pipeline:
Generic photogrammetry:
https://www.capturingreality.com/
https://github.com/alicevision/meshroom
I don't really understand what's unique about this patent, Structure from Motion has been around for ages, from video too. Maybe they have some optimisation around the processing, which traditionally is quite compute intensive
What I ask myself is why they do not use a form of projection like lasers. If I were to build a drone, it would have a ton of lasers...
You can also do point cloud with lidar from drones, just tends to be more expensive and heavier - https://enterprise.dji.com/news/detail/how-lidar-is-revoluti...
But sure, just having a good camera is probably a lot cheaper than using a projection of any form. Probably also significantly faster.
https://en.wikipedia.org/wiki/Wiggle_stereoscopy
If you got 2 good pictures (good subject at the right altitude and separation) you could put them into a stereoscopic program and create a nice stereogram (either red-blue colored or side-by-side, depending on your preferred viewing device).
What the software in the article does is compute all the parallax from multiple images to give essentially a 3D scan of the landscape. It has been theoretically possible for a long time, maybe this is one of the first to do it successfully in practice.
That old gif of the T-Rex from The Lost World is always a good example[1] but I suppose it's probably not licenced the right way for Wikipedia.
It all really depends on how close the subject is. You need to exaggerate the separation a little but not too much. In an airplane, the time between shots (and speed of course) determines the separation. If you're doing the same thing on the ground, it depends in how far your subject is, but you can step 1-2 feet sideways for your second shot. You just have to experiment to find what works.
I have tried on an iPhone before, but the default "slide" between photos isn't fast enough to get the effect, so I had to download them to my PC. Maybe there's an iOS app for it now.
Edited to add: I knew someone in the age of film who screwed 2 SLRs to a piece of wood about 16 inches apart, for 3d landscape photography. He used 2 mechanical remote shutter releases to make sure the photos were in sync. A similar rig with DLSRs and electronic remotes should be easy and more precise (to get same settings and synchronous release). I'm surprised I haven't seen it done yet.
Photogrammetry software has been commercially available for a while. I played around with Agisoft photoscan [0] some time ago, and while the results were impressive, it did take a lot of human interaction to select the right source images and parameters.
I think maybe The Army has automated this pipeline to go from drone camera -> 3d map without needing a human.
There's a 1000-ft hill behind my house, and I thought it would be cool to get a 3D print of it. You can do this now with some online apps, based on the terrain model that you see in google maps. But the public data is "only" 30m resolution (https://en.wikipedia.org/wiki/Shuttle_Radar_Topography_Missi...), whereas this technique could probably make a 1m resolution.
I produce 3D models from photogrammetry from drones for the Engineering & Construction industry in Australia. The free software to use is VisualSFM.
I recommend flying your drone over your target in a sort of 4-circle Venn diagram, with the overlapping center the actual subject you want to scan. Have the drone pointing inward to the center of each circle while flying, the camera being at a ~55deg angle. You're essentially circle-strafing in 4 circles with a little overlapping bit in the middle.
Take video footage of the whole trip and extract individual frames... can't remember if VisualSFM does that for you or not.
I've used the SparkPro iOS app (for mission planning and automated flight with a programmed set of waypoints with camera instructions).
And this service who'll stitch images together (for free for small enough areas. I needed a plot about 200 x 200m, and its didn't cost anything): https://www.mapsmadeeasy.com/
Takes a bit of thinking and experimenting to get a good result. You need to get 4 or more times overlap for everything you want good stitching and elevation data from. (And the DJI specs on their cameras are misleading. I wasn't expecting the FoV spec to be diagonal, so my first attempt didn't overlap as much as Idol intended...)
https://ibis.geog.ubc.ca/courses/geob373/lectures/Handouts/H...
British intelligence used overlapping photography to create a 3D map of occupied Europe during WW2. In more recent times, the maps were used to identify possible archaeological sites in Scotland.
You can do it handheld for sure, just make sure to have plenty of light and put the items on a small pedestal so you can get low angles of them without a table getting in the way. But for several hundred objects, it's going to take a while.
See also the duck from battle chess.
Sadly or Happily capitalism in particular venture capital investments in startups has largely surpassed them regarding the material resources part of the equation in recent times but they still suck significantly at the human coordination aspect of things.
Outdoor 3D reconstruction is actually easier, because outdoors you have highly varied and detailed scenes, which enables dense feature matching and accurate dense depth map calculations. Also, you probably have decent GPS priors* and a preplanned flight path, which makes localization of each RGB image pretty easy.
* This is a big advantage that usually can't be replicated indoors. GPS is more accurate outside, and the significantly larger flight path means that GPS' main issue, low precision, isn't as much of a barrier.
Now, for a military drone, I think they get better accuracy than 5 metres, but also the distances involved in an outdoor dataset mean that inaccuracies in the initial position estimate are much less significant.
You're thinking at the software level though. Look up 802.11mc though - that enables cm-level mapping.
Some research I did a few months ago was about trying to find ways to effectively bring in that advantage of outdoor photogrammetry into indoor environments, by piggybacking onto AR HMDs with built-in SLAM: https://www.youtube.com/watch?v=ldrGpGrOaZc