4 ms·

can binocular cameras replace lidar you think? they should result in just as reliable distance estimation

• loloquwowndueo 1y ago
  Ever heard of optical illusions? If a brain can be fooled by input from its two cameras like this, what hope does a dumb (or worse, artificially “intelligent”) computer have?
  • doctorhandshake 1y ago
    I think optical illusions are a poor choice to illustrate this point. They are manifestations of the corner cases, peculiarities, and side effects of our visual processing system and neither cameras nor Lidar are without their own analogous issues.
    • loloquwowndueo 1y ago
      If you’re saying cameras have analogous issues I fail to see how the analogy is a poor choice - looks to me like you understood exactly the point I was making.
• djaychela 1y ago
  No, they don't. Look at what has happened when a tesla has mistaken a motorcycle with two small rear lights that is nearby for a car that is further away but with the same lighting configuration. Did not end well for the motorcyclists. He's just wrong about this.
  • maxlin 1y ago
    [flagged]
    • djaychela 1y ago
      With respect, I disagree. Musk is obsessed with the "the best part is no part ". Which only works if you don't actually need it. Combined with an obsession with cost cutting, and you get tunnel vision insisting on a course of action which does not know with 100% certainty about the world it is trying to navigate. And this has led directly to people dying. Being obsessed only works when you turn out to be right, and tesla's system does not work as well as lidar.
  • Grimblewald 1y ago
    I dont think it's wrong, but i do think models avaliable right now lack the inductive bias required to solve the task appropriately, and have architectural misalignments with the task at hand that mean for a properly reliable output you'll need impossibly large models and impossibly large/varied datasets. Same goes for transformers for language modelling. Extremely adaptable model, but ultimately not aligned with the task of understanding and learning language, so we need enormous piles of data and huge models to get decent output.
• typewithrhythm 1y ago
  Can cameras do it eventually is a bit of a tangent. All the information is there in a video feed, but the amount of work to get reliable perception from it is not small. With LIDAR and radar you get to the end goal with less uncertainty.
  • rainsford 1y ago
    The real key is that things like LIDAR are designed to work well with the types of tasks computers are good at, like taking a bunch of precise measurements every second and performing complex calculations, while a binocular vision based understanding of the world is something humans are good at because we evolved that ability over millions of years. You can probably eventually ("never" is a long time after all) get a computer to understand the world as well as a human purely through camera based sensors, but it's a much more difficult task than taking an approach that uses tools computers are already good at. Similarly, I suspect it would be an uphill battle to have a human drive using raw LIDAR input.
    • 2OEH8eoCRo0 1y ago
      I think you underestimate how many guesses, approximations, and filling in your brain does to what you think you're seeing.
  • findthewords 1y ago
    Absolutely. The goal of self-driving is to be better than human drivers. Even the best drivers struggle with the sun shining from low angles, or road reflections, or snow, and so on.
• findthewords 1y ago
  They are complementary sensors. It's a much easier engineering feat to combine two (cheap) sensors that are good at different things and fusing this information than creating one perfect sensor that does everything. Private moon landers (the Japanese being most recent one) keep crashing because they rely on a single high-quality altimeter and expect it to work perfectly, all the time. If they had a complementary low-quality backup altimeter that operated independently, they would have had a less failure prone distance estimation system.