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Comment by phkahler | Hacker News Reader

>> For example, one of my favourite papers last year showed how to use geometry to learn depth with unsupervised training.

I've been saying that LIDAR is a hack for some time. People don't need it and neither should computers.

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I'm sympathetic to the idea that robotics should try to do more with cameras, as opposed to LiDAR. As you say, humans can do without LiDAR (and they don't even need stereo!). But in practice, there's a good case for LiDAR in real-world applications, at least for the near future:

* The accuracy of depth from a good LiDAR sensor is hard to beat in accuracy, specially at longer ranges. Looking at a stereo point cloud and a LiDAR point clouds side by side, you can really see the difference. * LiDAR works in bad lighting, even complete darkness. (In fact it might work better in such conditions). For self-driving applications, you probably want to be able to see that deer crossing the road at night. * LiDAR will work on textureless surfaces, that traditionally are an issue for stereo/SfM.

That said, there are plenty of issues with LiDAR: relatively sparse, doesn't work on certain surfaces like water or shiny metal, heavyweight (bad for drones). So at least for now, it makes sense to integrate with other sensing modalities.

Disclaimer: I have published research on Deep Learning with LiDAR.

Thanks for a solid response. One thing I've wondered about LIDAR though is how well does it work with tens or hundreds of systems operating in the same space? Do they interfere with each other? Think about one of those 6-8 lane highways packed full of cars each with a LIDAR system. Will it work? I honestly don't know. I have the same question about radars currently used for cruise control.

LIDAR is not a hack. It measures depth even when objects are not moving.

With vision we can estimate depth, but sometimes we need movement for that.

>> LIDAR is not a hack.

Depends on what one considers a "hack". Epistemologically, yes, it is a hack because it already assumes a 3D world identifiable via such means, and therefore may make the AI incapable of "reconstructing" in its "brain" more dimensions. Whereas if the AI has to learn the "hard way", it may be able to project that analogously to other ideas, and could "speak" of them in the same terms we do when speaking about dimensions.

Of course, if we're only looking for productivity from the AI, then philosophical "correctness" is a needless endeavor. ... But it might be more fun.

So, LIDAR is best solution for an autonomous vehicle when it is not moving?

I did not say that.

It might be the best solution when nothing is moving.

Our brains also cannot measure distance when there are unknown reference objects and everything is standing still.

That was a joke. Some people would laugh. Others down vote.

Why do dolphins need sonar ? They hunt in the visible light zone, they do have eyes. Same deal here.

Cats have whiskers. We have the advantage of being in the space we are judging relative to ourselves. LIDAR is analogous.

People don't need wheels. Cars shouldn't need them either.

You could build legs for cars. It's harder, yes, but it can be done. // Your argument doesn't work against his point. The fact is that yes, a computer could, in theory, gain a calculable 3D perspective of the world without the need for distance estimation scanning. It could start with the assumption already... but it helps to use two eyes.

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