  Visual sensors are not only becoming better at capturing high-quality images
but also they have steadily increased their capabilities in processing data on
their own on-chip. Yet the majority of VO pipelines rely on the transmission
and processing of full images in a centralized unit (e.g. CPU or GPU), which
often contain much redundant and low-quality information for the task. In this
paper, we address the task of frame-to-frame rotational estimation but, instead
of reasoning about relative motion between frames using the full images,
distribute the estimation at pixel-level. In this paradigm, each pixel produces
an estimate of the global motion by only relying on local information and local
message-passing with neighbouring pixels. The resulting per-pixel estimates can
then be communicated to downstream tasks, yielding higher-level, informative
cues instead of the original raw pixel-readings. We evaluate the proposed
approach on real public datasets, where we offer detailed insights about this
novel technique and open-source our implementation for the future benefit of
the community.
