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

2025-08-09

AI Summary

To address the fundamental trade-off between limited communication bandwidth and detection performance in multi-agent LiDAR-based collaborative 3D object detection, this paper proposes HyComm—a novel hybrid communication framework. Methodologically, HyComm introduces a mixed-message collaboration mechanism that jointly encodes perception outputs and raw LiDAR observations, enabling variable-rate compression and standardized message formatting. It further incorporates dual-path message selection and adaptive prioritization of critical data, coupled with lightweight compression strategies to ensure cross-model and cross-scenario generalizability. Evaluated on DAIR-V2X and OPV2V benchmarks, HyComm achieves over 2006× bandwidth reduction—far exceeding prior art—while significantly outperforming Where2comm in detection accuracy. Notably, it is the first method to achieve high-performance collaborative 3D detection under ultra-low-bandwidth constraints, establishing a new state of the art in bandwidth-efficient multi-agent perception.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 exttt{HyComm}, a communication-efficient LiDAR-based collaborative 3D detection system. exttt{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$ imes$ and still outperforms Where2comm on DAIR-V2X in terms of AP50. The related code will be released.

Problem

• Optimizing 3D detection performance with minimal communication bandwidth
• Adaptively integrating compact and rich data for efficient collaboration
• Ensuring model-agnostic message formats for diverse agent configurations

Innovation

• Hybrid collaboration integrates two message types
• Adaptive selection of critical data messages
• Standardized formats for model-independent communication