  Event cameras are a new type of sensors that are different from traditional
cameras. Each pixel is triggered asynchronously by event. The trigger event is
the change of the brightness irradiated on the pixel. If the increment or
decrement of brightness is higher than a certain threshold, an event is output.
Compared with traditional cameras, event cameras have the advantages of high
dynamic range and no motion blur. Since events are caused by the apparent
motion of intensity edges, the majority of 3D reconstructed maps consist only
of scene edges, i.e., semi-dense maps, which is not enough for some
applications. In this paper, we propose a pipeline to realize event-based dense
reconstruction. First, deep learning is used to reconstruct intensity images
from events. And then, structure from motion (SfM) is used to estimate camera
intrinsic, extrinsic and sparse point cloud. Finally, multi-view stereo (MVS)
is used to complete dense reconstruction.
