Abstract. In this paper we present ActiveStereoNet, the first deep
learning solution for active stereo systems. Due to the lack of ground
truth, our method is fully self-supervised, yet it produces precise depth
with a subpixel precision of 1/30th of a pixel; it does not suffer from the
common over-smoothing issues; it preserves the edges; and it explicitly
handles occlusions. We introduce a novel reconstruction loss that is more
robust to noise and texture-less patches, and is invariant to illumination
changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation
is edge-preserving and smooths the loss function, which is key to allow
the network to reach compelling results. Finally we show how the task
of predicting invalid regions, such as occlusions, can be trained end-toend without ground-truth. This component is crucial to reduce blur and
particularly improves predictions along depth discontinuities. Extensive
quantitatively and qualitatively evaluations on real and synthetic data
demonstrate state of the art results in many challenging scenes