Communication-Efficient Multi-Agent 3D Detection via Hybrid Collaboration

TL;DR

This paper introduces HyComm, a hybrid communication method for multi-agent 3D detection that adaptively combines compact perceptual outputs and raw observations, significantly reducing bandwidth while improving detection performance.

Contribution

The paper proposes a novel hybrid communication framework that adaptively integrates different message types for efficient multi-agent 3D detection, enhancing flexibility and performance.

Findings

HyComm reduces communication volume by over 2000 times.

HyComm outperforms previous methods on DAIR-V2X dataset.

It maintains high detection accuracy across diverse agent configurations.

Abstract

Collaborative 3D detection can substantially boost detection performance by allowing agents to exchange complementary information. It inherently results in a fundamental trade-off between detection performance and communication bandwidth. To tackle this bottleneck issue, we propose a novel hybrid collaboration that adaptively integrates two types of communication messages: perceptual outputs, which are compact, and raw observations, which offer richer information. This approach focuses on two key aspects: i) integrating complementary information from two message types and ii) prioritizing the most critical data within each type. By adaptively selecting the most critical set of messages, it ensures optimal perceptual information and adaptability, effectively meeting the demands of diverse communication scenarios.Building on this hybrid collaboration, we present \texttt{HyComm}, a communication-efficient LiDAR-based collaborative 3D detection system. \texttt{HyComm} boasts two main benefits: i) it facilitates adaptable compression rates for messages, addressing various communication requirements, and ii) it uses standardized data formats for messages. This ensures they are independent of specific detection models, fostering adaptability across different agent configurations. To evaluate HyComm, we conduct experiments on both real-world and simulation datasets: DAIR-V2X and OPV2V. HyComm consistently outperforms previous methods and achieves a superior performance-bandwidth trade-off regardless of whether agents use the same or varied detection models. It achieves a lower communication volume of more than 2,006$\times$ and still outperforms Where2comm on DAIR-V2X in terms of AP50. The related code will be released.