SAD runs in better than real time on a typical desktop CPU, even on a Pi. The author glossed over basically all of the calibration involved with stereo. It’s not quite as simple as taking two cameras and running a matcher.
Because stereo disparity (parallax) scales inversely with distance, conveniently you don’t need to search huge ranges. A point at infinity should have zero disparity in a calibrated stereo system. So normally you pick a depth range and search the corresponding pixels. Also if the cameras are calibrated, you only need to search horizontally so it works out to say 64 operations per pixel, which can be vectorized if the CPU supports it. Other approaches use more stable comparisons like census (is each pixel in the match window brighter than the central one) and hamming distance which is also very efficient.
And yes, stereo is sensitive to repeating texture but absence of texture like a blank wall is usually worse.
How long does it take? Wouldn’t there be a large range of offsets? Or does limiting the minimum distance help mitigate such?
I suppose there could be corner cases in which an object with regular pattern could be misinterpreted as being far away ?
Because stereo disparity (parallax) scales inversely with distance, conveniently you don’t need to search huge ranges. A point at infinity should have zero disparity in a calibrated stereo system. So normally you pick a depth range and search the corresponding pixels. Also if the cameras are calibrated, you only need to search horizontally so it works out to say 64 operations per pixel, which can be vectorized if the CPU supports it. Other approaches use more stable comparisons like census (is each pixel in the match window brighter than the central one) and hamming distance which is also very efficient.
And yes, stereo is sensitive to repeating texture but absence of texture like a blank wall is usually worse.