Stereo 3D Vision (How to avoid being dinner for Wolves) - Computerphile

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Published 2016-02-24
If you've wondered how computer scientists use pairs of cameras to reconstruct a 3D scene, Image Analyst & Lecturer Dr Mike Pound explains.

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This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at www.bradyharan.com/

All Comments (21)
  • @dvoraj20
    Frankly... this ended where I hoped it would start.
  • @Jader7777
    Best computerphile video:
    - Clean desk
    - Tidy shelf
    - Nice hair
    - Classic perforated printing paper
    - Popped collar
  • @DeJayHank
    Since pictures can be a bit noisy due to sensor imperfections, and the information gained from a single pixel isn't that much, Stereo vision algorithms often utilize Block Matching. It means that instead of finding a single pixel in the other image, you look at a "block" of pixels around it (a 5x5 block for example) and see if a very similar block can be found in the other picture. It is much more robust to single pixels being very distorted due to noise etc. but of course takes more computing time since now you have to work with 25 pixels for each matching test, instead of just 1.
  • @BGBTech
    I once did it with low enough CPU use to run (sort of) on a Raspberry Pi at ~ 10 fps, but it was pretty crude (output depth map was 80x60, and a bit noisy/glitchy. input was a pair of 320x240 images from parallel webcams).

    partial optimization was making use of blocky-VQ so that for a block of pixels (4x4), you can determine early if they are out of range (or are a nearly exact match). it was based on a trick I had used for doing motion compensation in video compression during capture (motion compensation helps somewhat with compression).

    the trick greatly reduced the amount of pixel-by-pixel checks. also it worked internally using a dichromatic colorspace, partly to save space and also because it was cheaper to only compare two axes (whereas a single Y axis loses the ability for it to distinguish things based on color, reducing accuracy somewhat).


    I had also tried unsuccessfully to use some designs based around Haar wavelets.
  • @unvergebeneid
    You can also move one eye/camera to get "actual" 3D because it's mathematically the same as two eyes for static scenes and under certain circumstances it even resembles the 3D "qualia" if you want to call it that.
  • @DaanLuttik
    Could you do one about how you account for reflective objects or objects in a space where lighting isn't ambient? I have some ideas on how one could do this. But I'd like to hear the smart ticks surrounding this topic.
  • @FabrizioBianchi
    Mike is the best speaker in the channel. AI guy comes a close second.
  • @Kruglord
    Great video, I think a good follow up video might be how people have approached the correspondence problem, such as using SIFT or SURF points.
  • @DeJayHank
    Would have liked if you showed an example of disparity map obtained from some stereo vision algorithm. It can help show what the result would look like.
  • @Peepnbrick
    I know you've already talked about color spaces, which was very interesting, but could you get Mike to do an episode on Gamma / Gamma Correction?
  • @Pr0toc01
    at 6:12 he is talking about its only possible because they know the positions of the 2 cameras, and that if they dont know the camera positions they have to search through the whole image.
    Question: Once you have done this one, two,...,ten times can you compute the cameras relative position?
    Once you calculate the cameras relative position you could then use that to make any future searches easier?
  • @WaqarRashid
    Its really helpful channel and had lots of interesting videos but I can't find some videos in order, some videos are hidden from the channel and there is only a small number of playlists.. Is there any website where I can access these videos in some order. Thanks.
  • @TheDrawdex
    I remember doing this for a final proyect. :D This would've been awesome.
  • @Spongman
    Monocular vision gets accurate depth from micro focus changes. Otherwise how does your eye know how to focus when you close one eye?
  • @TheAprone
    When the 3D picture was zoomed into the screen at 1:32, I just barely was able to make out the image before they started messing with it. It doesn't match the simulated "answer" they displayed a few seconds later.
  • @trunc8
    So finally, how is the occlusion problem solved(feature hidden in one view, existing in the other)?