3D reconstruction of old spaces which no longer exist seems like a clear use case to me. There's loads of old videos of driving down a street in the 80s, or neighborhoods in cities which got replaced.
I can imagine future iterations of this which bring together other stills of the same space at that time to augment the dataset. Then perhaps another pass to fill in gaps with likely missing content based on probability or data from say the same street 10 years later.
It won't be 100% real, but I think it'd be very cool to be able to have a google-street view style experience of areas before google street view existed.
I watched it ages ago, but I seem to remember one thing that I liked was that each time they changed the architecture, it was to solve a problem they had, or were beginning to have. They seemed to be staying away from pre-optimization and instead took the approach of tackling problems as they had as they appeared, rather than imagining problems long before/if they occurred.
It's a bit like the "perfect is the enemy of done" concept - you could spend 2-3x the time making it much more scalable, but that might have an opportunity cost which weakens you somewhere else or makes it harder/more expensive to maintain and support.
Take it with a pinch of salt, but I thought it seemed like quite a good level-headed approach to choosing how to spend time/money early on, when there's a lot of financial/time constraints.
I can imagine future iterations of this which bring together other stills of the same space at that time to augment the dataset. Then perhaps another pass to fill in gaps with likely missing content based on probability or data from say the same street 10 years later.
It won't be 100% real, but I think it'd be very cool to be able to have a google-street view style experience of areas before google street view existed.