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> despite the fact that a depth map is the easiest way to hide poor quality I did my PhD on stereo and LIDAR; also in industry building these things. This is s
by joshvm 3y ago
> despite the fact that a depth map is the easiest way to hide poor quality
I did my PhD on stereo and LIDAR; also in industry building these things. This is something that really annoyed us about camera companies selling stereo systems: you cannot tell anything from a colormapped depth plot. It doesn't matter if it's grayscale or jet or a nice perceptually uniform one. Zed are really bad for this in their promo material.
Small errors might be really significant for reconstruction, but depth maps make it easy to hide errors, fuzzy bits, holes, discontinuities etc. Really you want to test against a known calibration object at a distance and present the reconstruction error, show the 3D reconstruction top-down (or in a way which lets you see how much depth variation there is) or compare to a simultaneous LIDAR capture which might be sparse but will be more accurate (absolute) at distance.
- porphyra 3y agoA lot of lidar companies also show rainbow-colored point clouds from a perspective close to that of the sensor. So silly...
- hex4def6 3y agoI'm actually trying to objectively compare different stereo cameras, and I've been wondering if there is an "standard" calibration scene. I've been just looking at the depth maps to try and figure stuff like height accuracy, minimum separation between objects before they blob together, etc, but I'd like to know your thoughts on what a good method of comparison might be.
- joshvm 3y agoYou can buy known objects (expensive, for metrology calibration) or nowadays you could 3D print test objects. A simple one is to set up a flat board with white noise printed on it and then measure depth noise as a function of distance (eg fit a plane). The cameras usually aren't the problem. You just need two reasonably high quality machine vision cameras that are ideally hardware synced. There are geometric limits on how accurate you can be, related to the camera separation/baseline and how well you expect you can match at the sub pixel level. 0.1-0.25 px would be considered decent. Normally you'd design the problem in reverse eg what error do we need at the worst distance, field of view dictates lenses, etc. It can be very bespoke. Depth reconstruction is more reliant on matching algorithms and illumination. You can test stereo algorithms on benchmark datasets (The classics are Middlebury and KITTI). Illumination includes things like random dot projection or other artificial texture to aid matching + reconstruction.