Stereo error is quadratic with range. Bad times. You don't need high resolution, just a wide baseline for accuracy at distance. Resolution will only get you so

Stereo error is quadratic with range. Bad times. You don't need high resolution, just a wide baseline for accuracy at distance. Resolution will only get you so far because you can't get (and you wouldn't want) sensors with pixels below a micron in size. You usually can't change the focal length much because you have constraints on field of view, therefore the only viable option is more baseline. e_z = e_d * Z^2/(b*f) e_d is the disparity error (i.e. matching error) in metric units; i.e. a few microns, Z is distance, b is baseline, f is focal length It's relatively easy to estimate the accuracy you can achieve on a car because the maximum baseline is fixed to < 2m typically. You can plug in reasonable fields of view, sensors etc. Typically you can assume 0.25 px matching accuracy, assuming the algorithm does sub-pixel interpolation. Example: e_d = 2.2µm, Z=30m, b=1.5m, f=3mm (for a 2000px wide sensor that's 70 degrees FOV): e_z = ~40 cm. That's not too bad - enough to identify something big. At 100 m you'd have a disparity of 20 pixels. If you had a search radius of 256 px you'd have a close-range of 8m. Nowadays the compute problem isn't too bad - you just throw a GPU at it (or an FPGA, but you have memory limitations there). LIDAR has a more or less constant error with range, provided you've got a high enough signal to noise. However this does mean that at short distances, LIDAR is quite poor relatively.