PS$^2$F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing
This work addresses snapshot 3D sensing for applications like microscopy and imaging, offering a compact optical solution, though it is incremental as it builds on existing point spread function methods.
The authors tackled the problem of monocular depth estimation for complex scenes by engineering a polarized spiral point spread function, achieving up to 50% lower depth error compared to state-of-the-art designs with minimal loss in spatial resolution.
We propose a compact snapshot monocular depth estimation technique that relies on an engineered point spread function (PSF). Traditional approaches used in microscopic super-resolution imaging such as the Double-Helix PSF (DHPSF) are ill-suited for scenes that are more complex than a sparse set of point light sources. We show, using the Cramér-Rao lower bound, that separating the two lobes of the DHPSF and thereby capturing two separate images leads to a dramatic increase in depth accuracy. A special property of the phase mask used for generating the DHPSF is that a separation of the phase mask into two halves leads to a spatial separation of the two lobes. We leverage this property to build a compact polarization-based optical setup, where we place two orthogonal linear polarizers on each half of the DHPSF phase mask and then capture the resulting image with a polarization-sensitive camera. Results from simulations and a lab prototype demonstrate that our technique achieves up to $50\%$ lower depth error compared to state-of-the-art designs including the DHPSF and the Tetrapod PSF, with little to no loss in spatial resolution.