Long baseline stereo matching has a trade off of poor depth resolution in short ranges and requires wide angle lenses which result in an uneven distribution of

Long baseline stereo matching has a trade off of poor depth resolution in short ranges and requires wide angle lenses which result in an uneven distribution of data after correcting for distortion. The wider the lens, the more distortion increasing as you move away from the center of the lens as pixels are no longer square; you also need higher and higher resolution sensor to get good matches around the edges. This makes it very hard and computationally expensive because you need to search progressively larger areas of the image to do matching to get the same FOV at longer range. Large baseline also is more likely to suffer from occlusion (even self-occlusion) by objects in the foreground resulting in holes in the depth map. In addition to being harder to keep the cameras rigid between calibrations (but I guess they figured this part out). Instead of a depth map like they show it would be much more useful if they instead show a map of the reprojection error of a flat calibration target at the minimum and maximum depth ranges. Even if they are not aiming for geometric accuracy it would give a much better idea of the actual performance of their system. The problem with these stereo camera companies is they get obsessed with maximizing certain metrics when the fact is there is a fine balance of trade offs to maintain that is highly specific to the desired application and even scene. Sure, you can increase the baseline and figure out how to mitigate calibration but that creates many more problems to solve that only become apparent when you try to apply it to some application. Anyone interested in long stereo baseline should check out this blog which has been around forever: https://www.elphel.com/blog/2017/09/long-range-multi-view-stereo-camera-with-4-sensors/ https://www.elphel.com/blog/2017/09/long-range-multi-view-st...