WaveComm: Lightweight Communication for Collaborative Perception via Wavelet Feature Distillation

TL;DR

WaveComm introduces a wavelet-based communication method that significantly reduces data transmission in collaborative perception systems while maintaining high sensing performance, enabling scalable real-time multi-agent sensing in bandwidth-limited environments.

Contribution

The paper presents a novel wavelet feature distillation framework that minimizes communication load by transmitting only low-frequency components and reconstructing high-frequency details at the receiver.

Findings

Maintains state-of-the-art perception performance with over 86% reduction in communication volume.

Achieves competitive perception accuracy compared to existing methods.

Validates effectiveness through extensive experiments on multiple datasets.

Abstract

In multi-agent collaborative sensing systems, substantial communication overhead from information exchange significantly limits scalability and real-time performance, especially in bandwidth-constrained environments. This often results in degraded performance and reduced reliability. To address this challenge, we propose WaveComm, a wavelet-based communication framework that drastically reduces transmission loads while preserving sensing performance in low-bandwidth scenarios. The core innovation of WaveComm lies in decomposing feature maps using Discrete Wavelet Transform (DWT), transmitting only compact low-frequency components to minimize communication overhead. High-frequency details are omitted, and their effects are reconstructed at the receiver side using a lightweight generator. A Multi-Scale Distillation (MSD) Loss is employed to optimize the reconstruction quality across pixel, structural, semantic, and distributional levels. Experiments on the OPV2V and DAIR-V2X datasets for LiDAR-based and camera-based perception tasks demonstrate that WaveComm maintains state-of-the-art performance even when the communication volume is reduced to 86.3% and 87.0% of the original, respectively. Compared to existing approaches, WaveComm achieves competitive improvements in both communication efficiency and perception accuracy. Ablation studies further validate the effectiveness of its key components.