  The ubiquitous multi-camera setup on modern autonomous vehicles provides an
opportunity to construct surround-view depth. Existing methods, however, either
perform independent monocular depth estimations on each camera or rely on
computationally heavy self attention mechanisms. In this paper, we propose a
novel guided attention architecture, EGA-Depth, which can improve both the
efficiency and accuracy of self-supervised multi-camera depth estimation. More
specifically, for each camera, we use its perspective view as the query to
cross-reference its neighboring views to derive informative features for this
camera view. This allows the model to perform attention only across views with
considerable overlaps and avoid the costly computations of standard
self-attention. Given its efficiency, EGA-Depth enables us to exploit
higher-resolution visual features, leading to improved accuracy. Furthermore,
EGA-Depth can incorporate more frames from previous time steps as it scales
linearly w.r.t. the number of views and frames. Extensive experiments on two
challenging autonomous driving benchmarks nuScenes and DDAD demonstrate the
efficacy of our proposed EGA-Depth and show that it achieves the new
state-of-the-art in self-supervised multi-camera depth estimation.
