> This. Modern photogrammetry uses computer vision techniques like deep neural nets.
I'm surprised to hear that. What good do DNN do for photogrammetry? Aren't they too unreliable?
I only have university course-level exposure to this, most of it spent learning how to compute coordinate systems and transformation matrices from points on a set of pictures taken from different position. I guess DNNs could help with object isolation and labeling (identifying same objects on a set of pictures taken from different perspective)?
The Related work section in the DF-SLAM paper is great.
As you intuited, DNNs work great for tracking visual landmarks (better than traditional computed features) between frames. They can also estimate depth from a monocular camera, which is very useful.
A more out-of-left-field idea is training a CNN to directly regress the camera pose (position and orientation) from an image:
I'm surprised to hear that. What good do DNN do for photogrammetry? Aren't they too unreliable?
I only have university course-level exposure to this, most of it spent learning how to compute coordinate systems and transformation matrices from points on a set of pictures taken from different position. I guess DNNs could help with object isolation and labeling (identifying same objects on a set of pictures taken from different perspective)?