  Depth estimation from stereo images is carried out with unmatched results by
convolutional neural networks trained end-to-end to regress dense disparities.
Like for most tasks, this is possible if large amounts of labelled samples are
available for training, possibly covering the whole data distribution
encountered at deployment time. Being such an assumption systematically unmet
in real applications, the capacity of adapting to any unseen setting becomes of
paramount importance. Purposely, we propose a continual adaptation paradigm for
deep stereo networks designed to deal with challenging and ever-changing
environments. We design a lightweight and modular architecture, Modularly
ADaptive Network (MADNet), and formulate Modular ADaptation algorithms (MAD,
MAD++) which permit efficient optimization of independent sub-portions of the
entire network. In our paradigm, the learning signals needed to continuously
adapt models online can be sourced from self-supervision via right-to-left
image warping or from traditional stereo algorithms. With both sources, no
other data than the input images being gathered at deployment time are needed.
Thus, our network architecture and adaptation algorithms realize the first
real-time self-adaptive deep stereo system and pave the way for a new paradigm
that can facilitate practical deployment of end-to-end architectures for dense
disparity regression.
