  Stereo-based depth estimation is a cornerstone of computer vision, with
state-of-the-art methods delivering accurate results in real time. For several
applications such as autonomous navigation, however, it may be useful to trade
accuracy for lower latency. We present Bi3D, a method that estimates depth via
a series of binary classifications. Rather than testing if objects are at a
particular depth $D$, as existing stereo methods do, it classifies them as
being closer or farther than $D$. This property offers a powerful mechanism to
balance accuracy and latency. Given a strict time budget, Bi3D can detect
objects closer than a given distance in as little as a few milliseconds, or
estimate depth with arbitrarily coarse quantization, with complexity linear
with the number of quantization levels. Bi3D can also use the allotted
quantization levels to get continuous depth, but in a specific depth range. For
standard stereo (i.e., continuous depth on the whole range), our method is
close to or on par with state-of-the-art, finely tuned stereo methods.
