Event-based Shape from Polarization with Spiking Neural Networks
Peng Kang Srutarshi Banerjee Henry Chopp Aggelos Katsaggelos Oliver Cossairt
Recent advances in event-based shape determination from polarization offer a
transformative approach that tackles the trade-off between speed and accuracy
in capturing surface geometries. In this paper, we investigate event-based
shape from polarization using Spiking Neural Networks (SNNs), introducing the
Single-Timestep and Multi-Timestep Spiking UNets for effective and efficient
surface normal estimation. Specificially, the Single-Timestep model processes
event-based shape as a non-temporal task, updating the membrane potential of
each spiking neuron only once, thereby reducing computational and energy
demands. In contrast, the Multi-Timestep model exploits temporal dynamics for
enhanced data extraction. Extensive evaluations on synthetic and real-world
datasets demonstrate that our models match the performance of state-of-the-art
Artifical Neural Networks (ANNs) in estimating surface normals, with the added
advantage of superior energy efficiency. Our work not only contributes to the
advancement of SNNs in event-based sensing but also sets the stage for future
explorations in optimizing SNN architectures, integrating multi-modal data, and
scaling for applications on neuromorphic hardware.