poor-mans-vr VS dji-aerial-georeferencing

https://github.com/muxamilian/poor-mans-vr

A poor man's VR: Using the front camera and tensorflow.js, the smartphone becomes a “window” into the real world. Video and image content appear as if they were seen through this window. To do this, the viewer’s position is determined using a neural network. The viewed content is then moved according to the viewer’s position. This makes it seem like the content is physically behind the smartphone and is viewed through the smartphone’s screen. This effect is especially useful for content captured using an ultra-wide lens.

How does this compare with the data you get from Airdata?

When I was working on an aerial georeferencing project[1] I found that the on-device flight logs didn’t contain all the info in the serverside logs (eg heading, gimbal orientation, GPS coords, altitude)

[1] https://github.com/roboflow/dji-aerial-georeferencing

You can also do it if you know the height and field of view (I open sourced code to do this from drone videos here: https://blog.roboflow.com/georeferencing-drone-videos/ )

Yeah that’s pretty much it. I wrote about something similar for translating pixel coordinates from drone videos to GPS coordinates: https://blog.roboflow.com/georeferencing-drone-videos/

I'm not the OC (that'd be u/aloser). Based on the blog post breakdown (https://blog.roboflow.com/georeferencing-drone-videos/), it's a recorded video matched up with the flight log to identify the location of certain objects.

I published the code here: https://github.com/roboflow-ai/dji-aerial-georeferencing

Had a lot of fun building this; did an in-depth writeup here as well: https://blog.roboflow.com/georeferencing-drone-videos/