  Multiview detection incorporates multiple camera views to deal with
occlusions, and its central problem is multiview aggregation. Given feature map
projections from multiple views onto a common ground plane, the
state-of-the-art method addresses this problem via convolution, which applies
the same calculation regardless of object locations. However, such
translation-invariant behaviors might not be the best choice, as object
features undergo various projection distortions according to their positions
and cameras. In this paper, we propose a novel multiview detector, MVDeTr, that
adopts a newly introduced shadow transformer to aggregate multiview
information. Unlike convolutions, shadow transformer attends differently at
different positions and cameras to deal with various shadow-like distortions.
We propose an effective training scheme that includes a new view-coherent data
augmentation method, which applies random augmentations while maintaining
multiview consistency. On two multiview detection benchmarks, we report new
state-of-the-art accuracy with the proposed system. Code is available at
https://github.com/hou-yz/MVDeTr.
