"the strength of the return packet is generally directly proportional to depth (algorithmically" Err, no it isn't. You send a tx pulse and correlate it with a

"the strength of the return packet is generally directly proportional to depth (algorithmically" Err, no it isn't. You send a tx pulse and correlate it with a rx pulse and use the time of flight and the wave speed to get a range. You need precise information about where the tx pulse was sent to turn the range into 3d point relative to the sensor. The correlation and orientation steps are algorithmic and fundamenally statistical. For ultrasound and radar you can put a code in the pulse to make matching them up easier (the pulse lengths are typically bigger than the resolution you require). All algorithms. All can and do go wrong. Old school stereo matching looks at cross correlation (or cross entropy) of image patches. If the cameras are orthorectified (translated with no rotation) then you just scan across the row of pixels to find the patch with the best match and how far across it is is proportional to the depth. Feature correspondence (with sift like features, very low level) might be used for getting relative rotation and translation info between the cameras for calibration and as a depth prior. People are doing the same sort of thing with NNs to get the depth map.