  Collaborative perception enables agents to share complementary perceptual
information with nearby agents. This would improve the perception performance
and alleviate the issues of single-view perception, such as occlusion and
sparsity. Most existing approaches mainly focus on single modality (especially
LiDAR), and not fully exploit the superiority of multi-modal perception. We
propose a collaborative perception paradigm, BM2CP, which employs LiDAR and
camera to achieve efficient multi-modal perception. It utilizes LiDAR-guided
modal fusion, cooperative depth generation and modality-guided intermediate
fusion to acquire deep interactions among modalities of different agents,
Moreover, it is capable to cope with the special case where one of the sensors,
same or different type, of any agent is missing. Extensive experiments validate
that our approach outperforms the state-of-the-art methods with 50X lower
communication volumes in both simulated and real-world autonomous driving
scenarios. Our code is available at https://github.com/byzhaoAI/BM2CP.
