  Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in
matching tasks. However, all-pairs correlations lack non-local geometry
knowledge and have difficulties tackling local ambiguities in ill-posed
regions. In this paper, we propose Iterative Geometry Encoding Volume
(IGEV-Stereo), a new deep network architecture for stereo matching. The
proposed IGEV-Stereo builds a combined geometry encoding volume that encodes
geometry and context information as well as local matching details, and
iteratively indexes it to update the disparity map. To speed up the
convergence, we exploit GEV to regress an accurate starting point for ConvGRUs
iterations. Our IGEV-Stereo ranks $1^{st}$ on KITTI 2015 and 2012 (Reflective)
among all published methods and is the fastest among the top 10 methods. In
addition, IGEV-Stereo has strong cross-dataset generalization as well as high
inference efficiency. We also extend our IGEV to multi-view stereo (MVS), i.e.
IGEV-MVS, which achieves competitive accuracy on DTU benchmark. Code is
available at https://github.com/gangweiX/IGEV.
