  Full surround monodepth (FSM) methods can learn from multiple camera views
simultaneously in a self-supervised manner to predict the scale-aware depth,
which is more practical for real-world applications in contrast to
scale-ambiguous depth from a standalone monocular camera. In this work, we
focus on enhancing the scale-awareness of FSM methods for depth estimation. To
this end, we propose to improve FSM from two perspectives: depth network
structure optimization and training pipeline optimization. First, we construct
a transformer-based depth network with neighbor-enhanced cross-view attention
(NCA). The cross-attention modules can better aggregate the cross-view context
in both global and neighboring views. Second, we formulate a transformer-based
feature matching scheme with progressive training to improve the
structure-from-motion (SfM) pipeline. That allows us to learn scale-awareness
with sufficient matches and further facilitate network convergence by removing
mismatches based on SfM loss. Experiments demonstrate that the resulting
Scale-aware full surround monodepth (SA-FSM) method largely improves the
scale-aware depth predictions without median-scaling at the test time, and
performs favorably against the state-of-the-art FSM methods, e.g., surpassing
SurroundDepth by 3.8% in terms of accuracy at delta<1.25 on the DDAD benchmark.
