  This paper presents a novel self-supervised two-frame multi-camera metric
depth estimation network, termed M${^2}$Depth, which is designed to predict
reliable scale-aware surrounding depth in autonomous driving. Unlike the
previous works that use multi-view images from a single time-step or multiple
time-step images from a single camera, M${^2}$Depth takes temporally adjacent
two-frame images from multiple cameras as inputs and produces high-quality
surrounding depth. We first construct cost volumes in spatial and temporal
domains individually and propose a spatial-temporal fusion module that
integrates the spatial-temporal information to yield a strong volume
presentation. We additionally combine the neural prior from SAM features with
internal features to reduce the ambiguity between foreground and background and
strengthen the depth edges. Extensive experimental results on nuScenes and DDAD
benchmarks show M${^2}$Depth achieves state-of-the-art performance. More
results can be found in https://heiheishuang.xyz/M2Depth .
