Stereo camera disparity sensor


I want to set up a stereo camera so I can simulate the disparity. I think in an older version there was a disparity sensor but I can’t seem to find it now.

What I have done so far is set up two identical cameras, a left and a right. I have offset the right camera and the cross linked the two by giving their prim names.

Is this all I need to do to get a disparity sensor?

And if so, how do I display the results so I can check the output prior to creating the simulated data?

Oh and I am using 2022.2.0

Thanks for your help.

Yes, that is how you make a stereo camera, you need an external module to calculate the disparity, for example you can use this one:GitHub - NVIDIA-ISAAC-ROS/isaac_ros_dnn_stereo_disparity: Deep learned stereo disparity estimation

I was hoping that the disparity ground truth could be generated much the same way we can generate semantic segmentation mask rather than a estimate. Is there plans to add ground truth disparity outputs?

If setting the cameras as ‘stereo’ doesn’t give provide a disparity output can you explain the purpose of the cross-linking them? What is the difference between doing the cross linking and having two unlinked cameras if I need to calculate the disparity separately anyway? Especially since I can get the distance to the camera either as a separate output.

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In the case of the real stereo camera, most of the time stereo is used to estimate depth. And the depth estimation pipeline follows these steps:

  1. Stereo Input [2 x RGB images, with lens/camera distortions]
  2. Rectification [2 x RGB images]
  3. Stereo matching [Disparity image]
  4. Depth estimation [Depth image]

For a simulated camera sensor, like the ones supported in Isaac Sim, following outputs are normally available:

  1. Rectified camera output: RGB and Depth
  2. Simulated camera output [RGB, with lens/camera distortions]

In the case of the stereo camera, two sets of such images are available. Normally, this is sufficient to re-create the images at every step of the stereo-rectification-disparity-depth pipeline. For example:

  • Noisy depth or disparity image can be generated by passing the simulated camera output [RGB, with lens/camera distortions] through the regular pipeline above.
  • Disparity ground truth image can be generated from the simulated depth image by applying disparity = (baseline * focal length) / depth).