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> but yes, they should be able to solve that by vision alone. Not really. A purely vision-based system probably needs to see this example in training labeled

> but yes, they should be able to solve that by vision alone. Not really. A purely vision-based system probably needs to see this example in training labeled as "the back of a car" in order to differentiate it from reality. A sensor-diverse system could detect the metal on the car and determine it is a car without ever having seen such an example during training. And, this is just one edge case. The number of edge cases is infinite. The point in the article is that a diverse sensor array is advantageous because it will cover more edge cases. A purely vision based system will always have limitations compared to a system with more kinds of sensors.

marcosdumay 9y ago
> Not really. A purely vision-based system probably needs to see this example in training labeled as "the back of a car" in order to differentiate it from reality. Have you take a look at depth based feature recognition? It will automatically recognize everything as a car, and will tell the error of the 2D feature extraction every time. It's also extremely CPU intensive, so people don't apply it as often. It's a clear example of AI being hold down due to limitations in computer power.
- unityByFreedom 9y ago
  Why would you want to guess at a 3d mapping from a 2d image when you can get 3d data directly from a sensor, like lidar? Getting 3d from 2d images is certainly interesting and will have its applications. It's less accurate and more computationally demanding than using 3d input directly, however. Cars can and should have more than 2d input. The point of the article is that using a diverse array of sensors in cars will help the algorithm make better predictions. There are many edge cases out there that aren't covered,and we shouldn't expect engineers to plan for them all. What we want to do is throw a lot of raw input at an algorithm and run it through a ton of simulations. All steps of this are non-trivial, but with more data, it is easier.

marcosdumay 9y ago

> Not really. A purely vision-based system probably needs to see this example in training labeled as "the back of a car" in order to differentiate it from reality. Have you take a look at depth based feature recognition? It will automatically recognize everything as a car, and will tell the error of the 2D feature extraction every time. It's also extremely CPU intensive, so people don't apply it as often. It's a clear example of AI being hold down due to limitations in computer power.

unityByFreedom 9y ago
Why would you want to guess at a 3d mapping from a 2d image when you can get 3d data directly from a sensor, like lidar? Getting 3d from 2d images is certainly interesting and will have its applications. It's less accurate and more computationally demanding than using 3d input directly, however. Cars can and should have more than 2d input. The point of the article is that using a diverse array of sensors in cars will help the algorithm make better predictions. There are many edge cases out there that aren't covered,and we shouldn't expect engineers to plan for them all. What we want to do is throw a lot of raw input at an algorithm and run it through a ton of simulations. All steps of this are non-trivial, but with more data, it is easier.