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Everything you said is very wrong. LIDAR produces line of sight depth information. Stereo cameras produce line of sight depth information. Radar produces line o

by ebg13 8y ago

Everything you said is very wrong. LIDAR produces line of sight depth information. Stereo cameras produce line of sight depth information. Radar produces line of sight depth information. Ultrasound produces line of sight depth information. The only differences are how, how fast, and how detailed. The biggest difference between LIDAR and stereo vision is that LIDAR doesn't depend on illumination because it uses its own.

gamblor956 8y ago
Stereo cameras produce line of sight depth information That is wrong. Stereo cameras provide data which, after significant processing, can result in line-of-sight depth information. With radar (including LIDAR) and ultrasound, the depth information is essentially "free" since very little processing is required to extract the depth information (i.e., the information returned is the depth). So the biggest difference between stereovision and everything else is that stereovision requires additional processing to extract the depth information.
- colordrops 8y ago
  Aren't the algorithms for extracting depth from stereo imagery already solved and thus also essentially "free"?
  - codelord 8y ago
    Depth estimation is not a solved problem. Why do you think SDC companies pay so much to buy Lidar if depth estimation was solved?
    - unityByFreedom 8y ago
      We need to define solved. Used in the Musk sense, it is solved because it's possible. Used in the academic sense, it's not as reliable as depth from LiDAR.
      - rohit2412 8y ago
        This is the Crux of the problem. Elon musk and Tesla are happy to use words with misleading meaning deliberately. Autopilot, full self driving, "solved", safer than a human driver etc.
        natch 7y ago
        There's nothing wrong with using words. The real key is to understand carefully the context in which the words were used. The "misleading" accusation is just a projection people like you are imagining after cherry picking words without paying attention to the context. The words you are missing are things like: "will be," "in the future," "it is possible," "assuming regulatory approval," etc.
    - pbreit 8y ago
      Because they bet wrong. It happens.
      - shaklee3 8y ago
        Did that Tesla that smashed into the side of a semi truck bet correctly? https://www.google.com/amp/s/electrek.co/2016/07/01/understanding-fatal-tesla-accident-autopilot-nhtsa-probe/amp/ https://www.google.com/amp/s/electrek.co/2016/07/01/understa...
        areoform 8y ago
        ‘Did that Tesla that smashed into the side of a semi truck bet correctly?’ That’s needlessly antagonistic and not cool.
        studentrob 8y ago
        Antagonistic to whom? Tesla is using the public as guinea pigs. They can defend themselves with data. Customers/investors should ask them to share more than they do, which is a mere few sentences [1] [2]. [1] https://www.tesla.com/VehicleSafetyReport https://www.tesla.com/VehicleSafetyReport [2] https://www.youtube.com/watch?v=HqvatzjHGyk&t=47m17s https://www.youtube.com/watch?v=HqvatzjHGyk&t=47m17s
  - gamblor956 8y ago
    No, it's like this: Radar, LIDAR, ultrasound: returning packets have X value, where X is run through a single algorithm to return depth. In the case of ultrasound and radar, the strength of the return packet is generally directly proportional to depth (algorithmically). LIDAR systems might require an additional processing step depending on the type of LIDAR system used, since many systems use shifted-arrays or rotating frequencies. Stereovision: returns two matrices of data (i.e., each image). Each matrix is run through edge-detection and object-detection algorithms, and the resultant data is then run through more algorithms to match and compare objects between the two images. Finally, once objects have been matched up, a parallax algorithm determines their 3d positioning. While this can all be done in hardware (i.e., ASICS), it's still multiple additional levels of processing that must be done before you can even get the data to the decision-making part of the system. Stereovision is thus always inherently laggier than a radar-based system when it comes to determining depth.
    - ebg13 8y ago
      > Each matrix is run through edge-detection and object-detection algorithms, and the resultant data is then run through more algorithms to match and compare objects between the two images. Finally, once objects have been matched up, a parallax algorithm determines their 3d positioning. Where is your information coming from, and can you point me to a stereo algorithm that uses object detection?
      - jeffreygoesto 8y ago
        Right, almost all stereo depth calculation is matching without object detection. Maybe doing both simultaneously counts? ;-) https://arxiv.org/abs/1902.09738 https://arxiv.org/abs/1902.09738
    - pbreit 8y ago
      Sure it’s laggier but with dedicated silicon that’s a non-issue. Fact is vision is more than adequate. And lidar is literally blind.
      - 8y ago
- pbreit 8y ago
  Sure, but if you’ve got the processing it’s pretty easy.

by ebg13 8y ago

gamblor956 8y ago

Stereo cameras produce line of sight depth information That is wrong. Stereo cameras provide data which, after significant processing, can result in line-of-sight depth information. With radar (including LIDAR) and ultrasound, the depth information is essentially "free" since very little processing is required to extract the depth information (i.e., the information returned is the depth). So the biggest difference between stereovision and everything else is that stereovision requires additional processing to extract the depth information.

colordrops 8y ago
Aren't the algorithms for extracting depth from stereo imagery already solved and thus also essentially "free"?
- codelord 8y ago
  Depth estimation is not a solved problem. Why do you think SDC companies pay so much to buy Lidar if depth estimation was solved?
  - unityByFreedom 8y ago
    We need to define solved. Used in the Musk sense, it is solved because it's possible. Used in the academic sense, it's not as reliable as depth from LiDAR.
    - rohit2412 8y ago
      This is the Crux of the problem. Elon musk and Tesla are happy to use words with misleading meaning deliberately. Autopilot, full self driving, "solved", safer than a human driver etc.
      - natch 7y ago
        There's nothing wrong with using words. The real key is to understand carefully the context in which the words were used. The "misleading" accusation is just a projection people like you are imagining after cherry picking words without paying attention to the context. The words you are missing are things like: "will be," "in the future," "it is possible," "assuming regulatory approval," etc.
  - pbreit 8y ago
    Because they bet wrong. It happens.
    - shaklee3 8y ago
      Did that Tesla that smashed into the side of a semi truck bet correctly? https://www.google.com/amp/s/electrek.co/2016/07/01/understanding-fatal-tesla-accident-autopilot-nhtsa-probe/amp/ https://www.google.com/amp/s/electrek.co/2016/07/01/understa...
      - areoform 8y ago
        ‘Did that Tesla that smashed into the side of a semi truck bet correctly?’ That’s needlessly antagonistic and not cool.
        studentrob 8y ago
        Antagonistic to whom? Tesla is using the public as guinea pigs. They can defend themselves with data. Customers/investors should ask them to share more than they do, which is a mere few sentences [1] [2]. [1] https://www.tesla.com/VehicleSafetyReport https://www.tesla.com/VehicleSafetyReport [2] https://www.youtube.com/watch?v=HqvatzjHGyk&t=47m17s https://www.youtube.com/watch?v=HqvatzjHGyk&t=47m17s
- gamblor956 8y ago
  No, it's like this: Radar, LIDAR, ultrasound: returning packets have X value, where X is run through a single algorithm to return depth. In the case of ultrasound and radar, the strength of the return packet is generally directly proportional to depth (algorithmically). LIDAR systems might require an additional processing step depending on the type of LIDAR system used, since many systems use shifted-arrays or rotating frequencies. Stereovision: returns two matrices of data (i.e., each image). Each matrix is run through edge-detection and object-detection algorithms, and the resultant data is then run through more algorithms to match and compare objects between the two images. Finally, once objects have been matched up, a parallax algorithm determines their 3d positioning. While this can all be done in hardware (i.e., ASICS), it's still multiple additional levels of processing that must be done before you can even get the data to the decision-making part of the system. Stereovision is thus always inherently laggier than a radar-based system when it comes to determining depth.
  - ebg13 8y ago
    > Each matrix is run through edge-detection and object-detection algorithms, and the resultant data is then run through more algorithms to match and compare objects between the two images. Finally, once objects have been matched up, a parallax algorithm determines their 3d positioning. Where is your information coming from, and can you point me to a stereo algorithm that uses object detection?
    - jeffreygoesto 8y ago
      Right, almost all stereo depth calculation is matching without object detection. Maybe doing both simultaneously counts? ;-) https://arxiv.org/abs/1902.09738 https://arxiv.org/abs/1902.09738
  - pbreit 8y ago
    Sure it’s laggier but with dedicated silicon that’s a non-issue. Fact is vision is more than adequate. And lidar is literally blind.
    - 8y ago
pbreit 8y ago
Sure, but if you’ve got the processing it’s pretty easy.