CoPe: Taming Collaborative 3D Perception via Lite Network Attention across Mobile Agents

To extend the receptive field of a mobile agent in complex scenarios, it is essential for multiple agents to cooperate with each other. However, it is challenging to achieve comprehensive 3D perception at the minimal computational and communication costs. In this paper, we propose CoPe, a lightweight and efficient collaborative 3D perception scheme for mobile agents. The main idea of CoPe is for an ego agent to query the most helpful information from its neighboring agents through a lightweight network attention mechanism. To this end, at each agent, we first leverage Singular Value Decomposition (SVD) to decompose a full-size point cloud feature into components. Meanwhile, with the novel self-attention and cross-attention algorithms, we respectively select the key component of an ego agent that best represent the point cloud of the ego agent as a query, and valuable components of each helper agent that are most relevant to the query as the answer. After feature reconstruction and aggregation, an ego agent can have a comprehensive understanding about the scene and make accurate predictions on downstream tasks. CoPe is lightweight and can be easily implemented on mobile devices. Results of extensive experiments conducted on both real-world and simulation datasets demonstrate that CoPe can achieve superior 3D object detection accuracy while significantly reducing the incurred computational and communication costs.