Noise is a Good Teacher: A Noise-Driven Framework for Robust Collaborative Perception

摘要

Collaborative perception mitigates occlusions and extendsthe perception range, yet its accuracy remains susceptible to both poseand feature noise. Given these limitations, we propose NoiseGT, anoise-driven framework built upon the principle that “Noise is a GoodTeacher”. Instead of treating noise as a disturbance, NoiseGT leveragesit as a self-supervised signal to enhance robustness at both the poseand feature levels. Specifically, a pose calibration module first appliesmulti-level noise injection, followed by graph-based alignment refinement.This process enables the module to learn pose-invariant alignment de-spite localization uncertainties. Meanwhile, a feature denoising moduleadopts a “high-noise-over-low-noise” strategy to inject high-magnitudenoise into bird’s-eye-view (BEV) features, guiding the denoiser towardnoise-robust representations without relying on clean intermediate super-vision. Together, these components form a noise-driven learning processthat promotes consistent fusion across agents. At the most challengingnoise setting ((σt , σr ) = (1.2 m, 1.2◦ )), NoiseGT achieves state-of-the-artperformance over previous methods, improving AP@0.5/0.7 by a rela-tive 12.03%/14.56% on V2X-Sim, 2.67%/0.89% on DAIR-V2X, and6.12%/9.69% on OPV2V. These results highlight the effectiveness andgenerality of noise-driven learning for robust collaborative perception.