  Traditional depth sensors generate accurate real world depth estimates that
surpass even the most advanced learning approaches trained only on simulation
domains. Since ground truth depth is readily available in the simulation domain
but quite difficult to obtain in the real domain, we propose a method that
leverages the best of both worlds. In this paper we present a new framework,
ActiveZero, which is a mixed domain learning solution for active stereovision
systems that requires no real world depth annotation. First, we demonstrate the
transferability of our method to out-of-distribution real data by using a mixed
domain learning strategy. In the simulation domain, we use a combination of
supervised disparity loss and self-supervised losses on a shape primitives
dataset. By contrast, in the real domain, we only use self-supervised losses on
a dataset that is out-of-distribution from either training simulation data or
test real data. Second, our method introduces a novel self-supervised loss
called temporal IR reprojection to increase the robustness and accuracy of our
reprojections in hard-to-perceive regions. Finally, we show how the method can
be trained end-to-end and that each module is important for attaining the end
result. Extensive qualitative and quantitative evaluations on real data
demonstrate state of the art results that can even beat a commercial depth
sensor.
