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Noidea
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Depth estimation from images is a well established field and Blender is not the software to go for. Look for keywords like 3D reconstruction, structure-from-motion, multiview stereo, stereo reconstruction, stereo depth estimation. Typical approach will be to detect SIFT (or some other) features, match them, compute relative orientation of the cameras and then use something like semi-global global matching. The code you can find, for example, in OpenCV. Look at this tutorial, which creates a disparity map. There is also stereo_match.py in OpenCV-Python samples. For feature matching look here.

Depth estimation from images is a well established field and Blender is not the software to go for. Look for keywords like 3D reconstruction, structure-from-motion, multiview stereo, stereo reconstruction, stereo depth estimation. Typical approach will be to detect SIFT (or some other) features, compute relative orientation of the cameras and then use something like semi-global global matching. The code you can find, for example, in OpenCV. Look at this tutorial.

Depth estimation from images is a well established field and Blender is not the software to go for. Look for keywords like 3D reconstruction, structure-from-motion, multiview stereo, stereo reconstruction, stereo depth estimation. Typical approach will be to detect SIFT (or some other) features, match them, compute relative orientation of the cameras and then use something like semi-global global matching. The code you can find, for example, in OpenCV. Look at this tutorial, which creates a disparity map. There is also stereo_match.py in OpenCV-Python samples. For feature matching look here.

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Noidea
Noidea
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ThisDepth estimation from images is a well established field and Blender is not the software to go for. Look for keywords like 3D reconstruction, structure-from-motion, multiview stereo, stereo reconstruction, stereo depth estimation. Typical approach will be to detect SIFT (or some other) features, compute relative orientation of the cameras and then use something like semi-global global matching. The code you can find, for example, in OpenCV. You can lookLook at this tutorial.

This is a well established field and Blender is not the software to go for. Look for keywords like 3D reconstruction, structure-from-motion, multiview stereo, stereo reconstruction, stereo depth estimation. Typical approach will be to detect SIFT (or some other) features, compute relative orientation of the cameras and then use something like semi-global global matching. The code you can find in OpenCV. You can look at this tutorial.

Depth estimation from images is a well established field and Blender is not the software to go for. Look for keywords like 3D reconstruction, structure-from-motion, multiview stereo, stereo reconstruction, stereo depth estimation. Typical approach will be to detect SIFT (or some other) features, compute relative orientation of the cameras and then use something like semi-global global matching. The code you can find, for example, in OpenCV. Look at this tutorial.

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Noidea
Noidea
  • 1.3k
  • 1
  • 11
  • 31

This is a well established field and Blender is not the software to go for. Look for keywords like 3D reconstruction, structure-from-motion, multiview stereo, stereo reconstruction, stereo depth estimation. Typical approach will be to detect SIFT (or some other) features, compute relative orientation of the cameras and then use something like semi-global global matching. The code you can find in OpenCV. You can look at this tutorial.