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This is a research problem, not a compute problem. Directly trying to replicate what LIDAR gives you (SLAM, a 3d map of your current environment) using just cam

This is a research problem, not a compute problem. Directly trying to replicate what LIDAR gives you (SLAM, a 3d map of your current environment) using just cameras is still unsolved today. And it is an active area of research.

snovv_crash 10y ago
No, it is solved and there is commercial software that will do this for you. Look at Pix4D, AgiSoft, DroneDeploy, etc. The problem is that it typically doesn't run in real-time, and scales badly with number of images.
- robotresearcher 10y ago
  So it's not a solved problem then. Apart from realtime there is a robustness problem that the offline reconstruction systems don't suffer from too badly. Missing data and poor reconstructions are inconvenient for an offline system but could be deadly in a car.
  - snovv_crash 10y ago
    Parent said it is a research problem, not a compute problem. I asserted it is a compute problem because we already have robust 3D reconstruction from cameras, but it doesn't run in real-time. If you can come up with a 500GHz CPU with a GB of cache, the problem is solved. It is a compute problem, that we may be able to bypass with research.
    - argonaut 10y ago
      1) "Scales badly with the number of images" seems like a research problem to me. 2) In theory almost everything is "just" a compute problem (just enumerate or search over all possibilities - of course this may not finish for millenia). But if Moore's law says the compute is 20 years away, I think it's reasonable to call it a research problem.
toomuchtodo 10y ago
I agree it's a research project. Who better to perform said project than Tesla? They have 100k+ vehicles on the road being trained by drivers at a rate of over 2 million miles per day.
- deleted 10y ago
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- argonaut 10y ago
  Tesla does not have the world's experts in visual SLAM. Those people are in academia, or at Google/Microsoft. The 100k car/driver thing is a red herring. Steering angle / brake / acceleration info is not the kind of data you need for this task. Big useless data is still useless data, even if it's big. The right kind of data, if this is to be treated as an ML problem, is a 3D model of the environment (this would be the label), along with camera data (this would be the data). Ironically you need LIDAR to get those labels. It bears noting that the state of the art in visual SLAM is not ML based (it's really difficult to get the "ML" problem I just described to actually work - and it's not for lack of data, Google probably has petabytes of this data from their cars).
  - greglindahl 10y ago
    I thought that the data Tesla is collecting is things like "places where drivers always steer unexpectedly" or "places where drivers always stop but our system didn't detect a stop sign". Those are labels.
    - argonaut 10y ago
      Those are not the labels for the problem I was talking about (visual SLAM). Those are labels for a different problem.

snovv_crash 10y ago

No, it is solved and there is commercial software that will do this for you. Look at Pix4D, AgiSoft, DroneDeploy, etc. The problem is that it typically doesn't run in real-time, and scales badly with number of images.

robotresearcher 10y ago
So it's not a solved problem then. Apart from realtime there is a robustness problem that the offline reconstruction systems don't suffer from too badly. Missing data and poor reconstructions are inconvenient for an offline system but could be deadly in a car.
- snovv_crash 10y ago
  Parent said it is a research problem, not a compute problem. I asserted it is a compute problem because we already have robust 3D reconstruction from cameras, but it doesn't run in real-time. If you can come up with a 500GHz CPU with a GB of cache, the problem is solved. It is a compute problem, that we may be able to bypass with research.
  - argonaut 10y ago
    1) "Scales badly with the number of images" seems like a research problem to me. 2) In theory almost everything is "just" a compute problem (just enumerate or search over all possibilities - of course this may not finish for millenia). But if Moore's law says the compute is 20 years away, I think it's reasonable to call it a research problem.

toomuchtodo 10y ago

I agree it's a research project. Who better to perform said project than Tesla? They have 100k+ vehicles on the road being trained by drivers at a rate of over 2 million miles per day.

deleted 10y ago
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argonaut 10y ago
Tesla does not have the world's experts in visual SLAM. Those people are in academia, or at Google/Microsoft. The 100k car/driver thing is a red herring. Steering angle / brake / acceleration info is not the kind of data you need for this task. Big useless data is still useless data, even if it's big. The right kind of data, if this is to be treated as an ML problem, is a 3D model of the environment (this would be the label), along with camera data (this would be the data). Ironically you need LIDAR to get those labels. It bears noting that the state of the art in visual SLAM is not ML based (it's really difficult to get the "ML" problem I just described to actually work - and it's not for lack of data, Google probably has petabytes of this data from their cars).
- greglindahl 10y ago
  I thought that the data Tesla is collecting is things like "places where drivers always steer unexpectedly" or "places where drivers always stop but our system didn't detect a stop sign". Those are labels.
  - argonaut 10y ago
    Those are not the labels for the problem I was talking about (visual SLAM). Those are labels for a different problem.