  3D perception is a critical problem in autonomous driving. Recently, the
Bird-Eye-View (BEV) approach has attracted extensive attention, due to low-cost
deployment and desirable vision detection capacity. However, the existing
models ignore a realistic scenario during the driving procedure, i.e., one or
more view cameras may be failed, which largely deteriorates the performance. To
tackle this problem, we propose a generic Masked BEV (M-BEV) perception
framework, which can effectively improve robustness to this challenging
scenario, by random masking and reconstructing camera views in the end-to-end
training. More specifically, we develop a novel Masked View Reconstruction
(MVR) module for M-BEV. It mimics various missing cases by randomly masking
features of different camera views, then leverages the original features of
these views as self-supervision, and reconstructs the masked ones with the
distinct spatio-temporal context across views. Via such a plug-and-play MVR,
our M-BEV is capable of learning the missing views from the resting ones, and
thus well generalized for robust view recovery and accurate perception in the
testing. We perform extensive experiments on the popular NuScenes benchmark,
where our framework can significantly boost 3D perception performance of the
state-of-the-art models on various missing view cases, e.g., for the absence of
back view, our M-BEV promotes the PETRv2 model with 10.3% mAP gain.
