If the iPhone had LiDAR sensors, it would be possible to generate a 3D point cloud that you could theoretically use to furnish a 3D AR reconstruction of your surroundings. E.g. you could create an AR video chat whereby you see a "hologram" projection of the person you are speaking within your 3D environment.
However, given that LiDAR has yet to be released on iPhone, do you know of an alternative transformation algorithm that may be employed on a single video stream for consistent video depth estimation?
I came across this SIGGRAPH 2020 video depth estimation project which seems to do this pretty reliably. I think this question ultimately falls with the class of SLAM problems. The SIGGRAPH paper seems to be very promising, but ideally, this would be done in realtime (or with a small time lag), which doesn't seem plausible; this is a demo video in case you're curious.
Alternatively, I thought it might be possible using two cameras simultaneously, e.g. the wide and normal, to correct for the camera FOV and optic distortion in each and then use the 3D stereoscopic transform in OpenCV knowing the optical properties of the setup (distance between the cameras, focal length, etc.) to generate a 3D image of the scene with almost as much precision as LiDAR. This Medium article on Stereo 3D reconstruction seems to suggest that such a configuration is possible, although I suppose the small baseline distance between the cameras relative to the object might affect the outcome. I think depth map in Apple's AVFoundation is certainly an alternative; however, the TrueDepth camera system was only introduced with the iPhoneX.
Thanks in advance! I would appreciate it if this could be demonstrated by example :) I cross-posted this question on the Computer Graphics exchange.