  3D reconstruction from multiple views is a successful computer vision field
with multiple deployments in applications. State of the art is based on
traditional RGB frames that enable optimization of photo-consistency cross
views. In this paper, we study the problem of 3D reconstruction from
event-cameras, motivated by the advantages of event-based cameras in terms of
low power and latency as well as by the biological evidence that eyes in nature
capture the same data and still perceive well 3D shape. The foundation of our
hypothesis that 3D reconstruction is feasible using events lies in the
information contained in the occluding contours and in the continuous scene
acquisition with events. We propose Apparent Contour Events (ACE), a novel
event-based representation that defines the geometry of the apparent contour of
an object. We represent ACE by a spatially and temporally continuous implicit
function defined in the event x-y-t space. Furthermore, we design a novel
continuous Voxel Carving algorithm enabled by the high temporal resolution of
the Apparent Contour Events. To evaluate the performance of the method, we
collect MOEC-3D, a 3D event dataset of a set of common real-world objects. We
demonstrate the ability of EvAC3D to reconstruct high-fidelity mesh surfaces
from real event sequences while allowing the refinement of the 3D
reconstruction for each individual event.
