Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion

Jiajun Liang

Abstract:

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× speedup. The code is available at ...