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

翻译：目前最先进的偏振形状恢复（Shape-from-Polarization, SfP）解决方案受限于速度与分辨率之间的权衡：它们要么牺牲测量的偏振角度数量，要么因帧率限制而需要较长的采集时间，从而在精度或延迟方面有所妥协。我们利用事件相机来解决这一权衡问题。事件相机以微秒级分辨率运行，运动模糊可忽略不计，并输出连续的事件流，以异步方式精确测量光线随时间的变化。我们提出了一种设置：在事件相机前放置一个高速旋转的线性偏振片。我们的方法利用旋转引起的连续事件流，重建多个偏振角度的相对强度。实验表明，我们的方法优于基于帧的物理驱动基线，在合成数据集和真实世界数据集上，平均绝对误差（MAE）降低了25%。然而，在真实世界中，我们观察到具有挑战性的条件（即事件生成较少时）会损害物理驱动方法的性能。为解决这一问题，我们提出了一种基于学习的方法，该方法即使在低事件率下也能学习估计表面法向量，在真实世界数据集上，相较于物理驱动方法性能提升了52%。所提出的系统实现了相当于每秒50帧（50 fps）的采集速度（是商用偏振传感器帧率的两倍以上），同时保持了1MP的空间分辨率。我们的评估基于首个大规模事件型偏振形状恢复数据集。