  Event cameras can record scene dynamics with high temporal resolution,
providing rich scene details for monocular depth estimation (MDE) even at
low-level illumination. Therefore, existing complementary learning approaches
for MDE fuse intensity information from images and scene details from event
data for better scene understanding. However, most methods directly fuse two
modalities at pixel level, ignoring that the attractive complementarity mainly
impacts high-level patterns that only occupy a few pixels. For example, event
data is likely to complement contours of scene objects. In this paper, we
discretize the scene into a set of high-level patterns to explore the
complementarity and propose a Pattern-based Complementary learning architecture
for monocular Depth estimation (PCDepth). Concretely, PCDepth comprises two
primary components: a complementary visual representation learning module for
discretizing the scene into high-level patterns and integrating complementary
patterns across modalities and a refined depth estimator aimed at scene
reconstruction and depth prediction while maintaining an efficiency-accuracy
balance. Through pattern-based complementary learning, PCDepth fully exploits
two modalities and achieves more accurate predictions than existing methods,
especially in challenging nighttime scenarios. Extensive experiments on MVSEC
and DSEC datasets verify the effectiveness and superiority of our PCDepth.
Remarkably, compared with state-of-the-art, PCDepth achieves a 37.9%
improvement in accuracy in MVSEC nighttime scenarios.
