Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
CoRR(2023)
摘要
It is widely believed that the dense supervision is better than the sparse
supervision in the field of depth completion, but the underlying reasons for
this are rarely discussed. In this paper, we find that the challenge of using
sparse supervision for training Radar-Camera depth prediction models is the
Projection Transformation Collapse (PTC). The PTC implies that sparse
supervision leads the model to learn unexpected collapsed projection
transformations between Image/Radar/LiDAR spaces. Building on this insight, we
propose a novel ``Disruption-Compensation" framework to handle the PTC, thereby
relighting the use of sparse supervision in depth completion tasks. The
disruption part deliberately discards position correspondences among
Image/Radar/LiDAR, while the compensation part leverages 3D spatial and 2D
semantic information to compensate for the discarded beneficial position
correspondence. Extensive experimental results demonstrate that our framework
(sparse supervision) outperforms the state-of-the-art (dense supervision) with
11.6$\%$ improvement in mean absolute error and $1.6 \times$ speedup. The code
is available at ...
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