MSU Device Testing - determination of the 3D devices’ characteristics: Participate

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You can help the project via testing a 3D device you have. You will receive a detailed report for the device. You will also get a map of 3D-perception quality per viewing position.

What should you do to help us?

  1. Inform us if you want to test your device. Additional information is very pleased to achieve best testing:
    • the type of equipment (i.e., a system with two projectors using linear polarization technology; 28-views autostereoscopic display using only format 2D + Z)
    • the model of equipment (i.e., Philips WowVX 42”)
    • the screen resolution recommended by the producer (i.e., 3840×2160)
  2. You will receive response to the letter which will include:
    • the detailed instructions how to perform the test
    • the set of the test images that are designed specifically for your device
  3. You will need to attach special labels to the equipment. It is necessarily to determine camera position in automatic way (see for details into sent instructions).
  4. You will have to display test images (received from us before) in a full screen mode than you will have to take some photos of equipment from different locations.
  5. Send us the archive with captured photos. (you may want to use one of the public sharing services: Google Drive, Yandex Drive, Dropbox, etc.)
  6. Wait a few days and get the result of the test
  7. As a result — the world have become a little better!

Please, write explicitly if you really don’t want to publish in public the result of your device testing.
You can contact us at: 3DDeviceTest@graphics.cs.msu.ru

The things which may be required

Tips how to capture your device

18 Mar 2013
See Also
Call for HEVC codecs 2019
Fourteen modern video codec comparison
Parallax range estimation in S3D video
Parallax determines the depth of S3D movies. The range of parallaxes should be both comfortable and entertaining for spectators.
Geometric distortions analysis and correction
Production of low-budget movies is prone to errors. Our method automatically corrects rotation and scale mismatch.
Automatic detection of artifacts in converted S3D videos
Our set of algorithms detects edge sharpness mismatch, cardboard effect, and crosstalk noticeability.
Temporal shift estimation for stereoscopic videos
How to take into account geometric distortions in the estimation of the temporal shift?
Neural network-based algorithm for classification of stereoscopic video by the production method
What method was used to create the 3D scene?
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