  Depth map estimation from images is an important task in robotic systems.
Existing methods can be categorized into two groups including multi-view stereo
and monocular depth estimation. The former requires cameras to have large
overlapping areas and sufficient baseline between cameras, while the latter
that processes each image independently can hardly guarantee the structure
consistency between cameras. In this paper, we propose a novel multi-camera
collaborative depth prediction method that does not require large overlapping
areas while maintaining structure consistency between cameras. Specifically, we
formulate the depth estimation as a weighted combination of depth basis, in
which the weights are updated iteratively by a refinement network driven by the
proposed consistency loss. During the iterative update, the results of depth
estimation are compared across cameras and the information of overlapping areas
is propagated to the whole depth maps with the help of basis formulation.
Experimental results on DDAD and NuScenes datasets demonstrate the superior
performance of our method.
