[pretty_dumm_guy]

> The frustrating thing about my project is the sheer amount of computation required. I really don't need a direct photogrammetry capture of a trail, an approximation would be fine to some degree. But I take like ten gigabytes of video data and then process each frame to find keypoints, run correlation on all these points, and all this (using COLMAP). This stuff can take days to process on my desktop.

If the hiking trails are accessible enough, you should have a look at SLAM technique. SLAM allows you to create smooth and rough approximate map of the environment through which you navigate your camera. Colorization of this map could be done by a GAN(might be an interesting side project).

I am adding some pointers below :

1. https://www.doc.ic.ac.uk/~ajd/ - Prof. Davison and his group's work is impressive in this area. 2. https://vision.in.tum.de/research/vslam - Prof. Cremers group have some SOTA algorithms in this area.

P.S: You don't need a heavy setup for this. A single or a stereo camera should do the job.

[TaylorAlexander]

Thank you very much! It has been a long time since I've looked at visual slam actually. That Omnidirectional LSD-SLAM looks really nice. Their code repo has been untouched for six years, but this still makes me realize I need to use vslam! I just found this recent work which seems really useful.

https://github.com/ivalab/gf_orb_slam2

I feel like vslam could be the the first step in a post-processing pipeline that would reduce a lot of the computational complexity of solving large maps. Once I can easily make large maps I can build simulated environments and use those for training an agent.

Thanks again for the tips!