31–40 of 142 posts
This completely fails to address Musk's argument: that for a L5 car you need to be able to drive in inclement weather where LIDAR does not work reliably. Musk may be right or wrong, but this article is a non-sequitur.
So what was Musk's point ?
This completely neglects the fact that humans can build near perfect 3D representations of the world with 2D images stitched together with the parallax neural nets in our brain. This blogpost briefly mentions it in one line as a throwaway and says you'd need extremely high resolution cameras?? Doesn't make sense at all. Two cameras of any resolution spaced a regular distance apart should be able to build a better par…
With cameras and computer vision there's no way to prove it. There is always a chance that it will glitch out for a second and kill someone.
I suspect this is yet another story sponsored by the Tesla shorts. Just saw an excellent two hour interview by Lex Fridman of George Hotz and he goes into details why he thinks camera will win over lidar. But he also admits that presently Google is ahead of everyone in the race for level 5, but raises the question of whether they can ever do it economically enough to make money on it? https://www.youtube.com/watch?v=…
I believe Tesla’s also have radar. I also listened to the podcast. George made it sound like the Lidar wasn’t being used for much. It augments the maps to help determine more precise location?
The point Musk and others are making though is that the lidar on the market today has poor performance in weather. The cameras will struggle to a degree in weather as well, so not having good annotations when your dev car is driving though rain is exactly the time when you need the ground-truth to be as clean as possible.
https://news.ycombinator.com/item?id=20677720
https://news.ycombinator.com/item?id=20680495
https://news.ycombinator.com/item?id=20683288
This completely neglects the fact that humans can build near perfect 3D representations of the world with 2D images stitched together with the parallax neural nets in our brain. This blogpost briefly mentions it in one line as a throwaway and says you'd need extremely high resolution cameras?? Doesn't make sense at all. Two cameras of any resolution spaced a regular distance apart should be able to build a better par…
You cannot have false negatives. Ever. You cannot have a situation where the system doesn't see a pedestrian and runs over them at without noticing. So you need to make a very convincing argument that it can't happen. With cameras and computer vision there's no way to prove it. There is always a chance that it will glitch out for a second and kill someone.
This is the main takeaway. Unsurprising but interesting nonetheless. I'm working in the field and it confirms my experience.
However they have a big bias that need to be pointed out:
[...] we must be able to annotate this data at extremely high accuracy levels or the perception system’s performance will begin to regress.
Since Scale has a suite of data labeling products built for AV developers, [...]
Garbage in, garbage out; yes annotation quality matters. But they're neglecting very promising approaches that allow to leverage non-annotated datasets (typically standard rgb images) to train models, for example self-supervised learning from video. A great demonstration of the usefulness of self-supervision is monocular depth estimation: taking consecutive frames (2D images) we can estimate per pixel depth and camera ego-motion by training to wrap previous frames into future ones. The result is a model capable of predicting depth on individual 2D frames. See this paper [1][2] for example.
By using this kind of approach, we can lower the need for precisely annotated data.
[1] https://arxiv.org/abs/1904.04998
[2] more readable on mobile: https://www.arxiv-vanity.com/papers/1904.04998/
Edit: typo
This completely neglects the fact that humans can build near perfect 3D representations of the world with 2D images stitched together with the parallax neural nets in our brain. This blogpost briefly mentions it in one line as a throwaway and says you'd need extremely high resolution cameras?? Doesn't make sense at all. Two cameras of any resolution spaced a regular distance apart should be able to build a better par…
> near perfect 3D representations of the world with 2D images This is ridiculous. I am sitting in front of a monitor right now. Please explain how I can perfectly determine the depth of it even though I can't see behind it ? I can move my ahead all around it to capture hundreds of different viewpoints but a car can't do that.
This completely neglects the fact that humans can build near perfect 3D representations of the world with 2D images stitched together with the parallax neural nets in our brain. This blogpost briefly mentions it in one line as a throwaway and says you'd need extremely high resolution cameras?? Doesn't make sense at all. Two cameras of any resolution spaced a regular distance apart should be able to build a better par…
The first thing we need to remember is the self driving doesn't work like our brain. If they do then we don't need to train them with billions of images. So the main problem is not just building the 3d models. For example we don't crash into the car because we never seen that car model or that kind of vehicle before. Check https://cdn.technologyreview.com/i/images/bikeedgecasepredic... we never think that there is a…
Also provides a reliable source of data, if humans have a LiDAR in their system then we would use it to improve our decisions.
I don’t see why we should limit the AV.
And the title is inflammatory. Nobody who understands the discussion is talking only about camera versus lidar. It’s more about camera+radar versus camera+radar+lidar, and other comparisons between other hybrid or standalone sensor combinations. It’s not as simple as one versus the other... surprised we still have to point this out to them.