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289 points sandslash | 1 comments | | HN request time: 0.241s | source
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jgord ◴[] No.44452103[source]
makes sense - humans have evolved a lot of wetware dedicated to 3D processing from stereo 2D.

I've made some progress on a PoC in 3D reconstruction - detecting planes, edges, pipes from pointclouds from lidar scans, eg : https://youtu.be/-o58qe8egS4 .. and am bootstrapping with in-house gigs as I build out the product.

Essentially it breaks down to a ton of matmulls, and I use a lot of tricks from pre-LLM ML .. this is a domain that perfectly fits RL.

The investors Ive talked to seem to understand that scan-to-cad is a real problem with a viable market - automating 5Bn / yr of manual click-labor. But they want to see traction in the form of early sales of the MVP, which is understandable, especially in the current regime of high interest rates.

Ive not been able to get across to potential investors the vast implications for robotics, AI, AR, VR, VFX that having better / faster / realtime 3D reconstruction will bring. Its great that someone of the caliber of Fei-Fei Li is talking about it.

Robots that interact in the real world will need to make a 3D model in realtime and likely share it efficiently with comrades.

While a gaussian splat model is more efficient than a pointcloud, a model which recognizes a wall as a quad plane is much more efficient still, and needed for realtime communication. There is the old idea that compression is equivalent to AI.

What is stopping us from having a google street-view v3.0 in which I can zoom right into and walk around a shopping mall, or train station or public building ? Our browsers can do this now, essentially rendering quake like 3D environments - the problem is with turning a scan into a lightweight 3D model.

Photogrammetry, where you have hundreds of photos and reconstruct the 3D scene, uses a lot of compute, and the colmap / Structure-from-Motion algorithm predates newer ML approaches and is ripe for a better RL algorithm imo. Ive done experiments where you can manually model a 3D scene from well positioned 360 panorama photos of a building, picking corners, following the outline of walls to make a floorplan etc ... this should be amenable to an RL algorithm. Most 360 panorama photo tours have enough overlap to reconstruct the scene reasonably well.

I have no doubt that we are on the brink of a massive improvement in 3D processing. Its clearly solvable with the ML/RL approaches we currently have .. we dont need AGI. My problem is getting funding to work on it fulltime, equivalently talking an investor into taking that bet :)

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1. jgord ◴[] No.44452259[source]
ps. its handy to compare the relative data sizes of [ models of ] the same scene : typically for something like a house, the data will be ballpark :

  -  15GB of pointcloud data ( 100Mn xyzRGB points from a lidar laser scanner )
  -  3 GB of 360 panorama photos
  -  50MB obj 3D textured model
  -  2MB CAD model
Im guessing gaussian-splat would be something like 20x to 40x more efficient than the pointcloud. I achieved similar compression for building scans, using flat textured mini-planes.