  We study the problem of estimating optical flow from event cameras. One
important issue is how to build a high-quality event-flow dataset with accurate
event values and flow labels. Previous datasets are created by either capturing
real scenes by event cameras or synthesizing from images with pasted foreground
objects. The former case can produce real event values but with calculated flow
labels, which are sparse and inaccurate. The later case can generate dense flow
labels but the interpolated events are prone to errors. In this work, we
propose to render a physically correct event-flow dataset using computer
graphics models. In particular, we first create indoor and outdoor 3D scenes by
Blender with rich scene content variations. Second, diverse camera motions are
included for the virtual capturing, producing images and accurate flow labels.
Third, we render high-framerate videos between images for accurate events. The
rendered dataset can adjust the density of events, based on which we further
introduce an adaptive density module (ADM). Experiments show that our proposed
dataset can facilitate event-flow learning, whereas previous approaches when
trained on our dataset can improve their performances constantly by a
relatively large margin. In addition, event-flow pipelines when equipped with
our ADM can further improve performances.
