3 ms·
The reason is that the underlying matrices are rank deficient and the calculation gets numerically unstable to impossible, if you don't vary all kinds of degree
by jeffreygoesto 4y ago
The reason is that the underlying matrices are rank deficient and the calculation gets numerically unstable to impossible, if you don't vary all kinds of degrees of freedom in the motion. Just changing one axis is not enough. If you look at 3D reconstruction image sequences from around 2000, you get motion sick from looking at them, because they made sure to always also vary rotation and translation in more than one direction. One video with some explanations I found:
https://youtu.be/AQfRdr_gZ8g https://youtu.be/AQfRdr_gZ8g
- Enginerrrd 4y agoThis sounds correct to me and aligns with a lot of other results from my experiments. I thank you for the insight. The only think I'll add is that it can't be an impossible task, because I can look at the same sequence of images and get a pretty good understanding of the 3D topography. That said, some things DON'T come through very well, like slopes so I think starting with priors that contain the gimbal position in addition to the GPS-derived location would really help pick up slopes, if that information were actually included in the pipeline. As advanced as it all is right now, computer vision still has a long way to go I think, with some very fruitful ideas yet to be implemented.
- jeffreygoesto 4y agoI think that is the strength of the deep learning methods that have been thrown at the problem in the meantime. They learn to pick a reasonable choice out of the null space by looking at the scene. Only good if the assumptions encoded into the network actually hold though. Kind of applies to us deep human networks as well. That's why optical illusions are fun to see.