This is cool. Very well done. I wish I needed it for something, I'd take one for sure.
BobbyTables2 2 hours ago [-]
The SAD algorithm looks neat!
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 ?
throwaway219450 2 hours ago [-]
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.
hex4def6 38 minutes ago [-]
I'm not convinced he's done the calibration, at least in his demo video. It's pretty bad, imo.
I think he's just running it on the raw unrectified images.
For anyone wanting to play with this, OpenCV has a number of tutorials on this using stuff like stereoBM (which also uses SAD if I'm remembering my algos).
The first step is calibration -- intrinsics (basically figuring out the equations that can cancel out the lens distortion) and extrinsics (how far apart are the two cameras in XYZ, tilt, etc).
a_t48 1 hours ago [-]
Can also help if you can remove IR filter + add an IR noise projector
numpad0 20 minutes ago [-]
... why aren't there mentions of stereo baseline distance and clock signal distribution challenges?
Also can't you just buy the PS5 camera? IIRC they switched to regular USB plugs for PS5 with adapters available for PS4 cameras.
ThrowawayTestr 4 hours ago [-]
Neat! What's the BOM cost?
Rendered at 06:07:40 GMT+0000 (Coordinated Universal Time) with Vercel.
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.
I think he's just running it on the raw unrectified images.
For anyone wanting to play with this, OpenCV has a number of tutorials on this using stuff like stereoBM (which also uses SAD if I'm remembering my algos).
The first step is calibration -- intrinsics (basically figuring out the equations that can cancel out the lens distortion) and extrinsics (how far apart are the two cameras in XYZ, tilt, etc).
Also can't you just buy the PS5 camera? IIRC they switched to regular USB plugs for PS5 with adapters available for PS4 cameras.