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Conventional stereo suffers from a fundamental trade-off between imagingvolume and signal-to-noise ratio (SNR) -- due to the conflicting impact ofaperture size on both these variables. Inspired by the extended depth of fieldcameras, we propose a novel end-to-end learning-based technique to overcomethis limitation, by introducing a phase mask at the aperture plane of thecameras in a stereo imaging system. The phase mask creates a depth-dependentpoint spread function, allowing us to recover sharp image texture and stereocorrespondence over a significantly extended depth of field (EDOF) thanconventional stereo. The phase mask pattern, the EDOF image reconstruction, andthe stereo disparity estimation are all trained together using an end-to-endlearned deep neural network. We perform theoretical analysis andcharacterization of the proposed approach and show a 6x increase in volume thatcan be imaged in simulation. We also build an experimental prototype andvalidate the approach using real-world results acquired using this prototypesystem.

CodedStereo: Learned Phase Masks for Large Depth-of-field Stereo