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Currently, I am working at a short project about stereo-vision.

I'm trying to create depth maps of a scenery. For this, I use my phone from to view points and use the following code/workflow provided by Matlab : https://nl.mathworks.com/help/vision/ug/uncalibrated-stereo-image-rectification.html

Following this code I am able to create nice disparity maps, but I want to now the depths (as in meters). For this, I need the baseline, focal length and disparity, as shown here: https://www.researchgate.net/figure/Relationship-between-the-baseline-b-disparity-d-focal-length-f-and-depth-z_fig1_2313285

The focal length and base-line are known, but not the baseline. I determined the estimate of the Fundamental Matrix. Is there a way to get from the Fundamental Matrix to the baseline, or by making some assumptions to get to the Essential Matrix, and from there to the baseline.

I would be thankful for any hint in the right direction!

raymond
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1 Answers1

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"The focal length and base-line are known, but not the baseline."

I guess you mean the disparity map is known. Without a known or estimated calibration matrix, you cannot determine the essential matrix. (Compare Multi View Geometry of Hartley and Zisserman for details)

With respect to your available data, you cannot compute a metric reconstruction. From the fundamental matrix, you can only extract camera matrices in a canonical form that allow for a projective reconstruction and will not satisfy the true baseline of the setup. A projective reconstruction is a reconstruction that differs from the metric result by an unknown transformation. Non-trivial techniques could allow to upgrade these reconstructions to an Euclidean reconstruction result. However, the success of these self-calibration techniques strongly depends of the quality of the data. Thus, using images of a calibrated camera is actually the best way to go.

Miau
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