  State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a
speed-resolution tradeoff: they either sacrifice the number of polarization
angles measured or necessitate lengthy acquisition times due to framerate
constraints, thus compromising either accuracy or latency. We tackle this
tradeoff using event cameras. Event cameras operate at microseconds resolution
with negligible motion blur, and output a continuous stream of events that
precisely measures how light changes over time asynchronously. We propose a
setup that consists of a linear polarizer rotating at high-speeds in front of
an event camera. Our method uses the continuous event stream caused by the
rotation to reconstruct relative intensities at multiple polarizer angles.
Experiments demonstrate that our method outperforms physics-based baselines
using frames, reducing the MAE by 25% in synthetic and real-world dataset. In
the real world, we observe, however, that the challenging conditions (i.e.,
when few events are generated) harm the performance of physics-based solutions.
To overcome this, we propose a learning-based approach that learns to estimate
surface normals even at low event-rates, improving the physics-based approach
by 52% on the real world dataset. The proposed system achieves an acquisition
speed equivalent to 50 fps (>twice the framerate of the commercial polarization
sensor) while retaining the spatial resolution of 1MP. Our evaluation is based
on the first large-scale dataset for event-based SfP
