CATNet：面向协同感知的协同对齐与变换网络
CATNet: Collaborative Alignment and Transformation Network for Cooperative Perception
面向车路协同/多智能体感知中的时序延迟与多源噪声问题，STSync 的相邻帧差分对齐与 WTDen 的小波域去噪可作为即插模块复用到任何异步多视角特征融合流程中。
原文依据 we propose Collaborative Alignment and Transformation Network (CATNet), an adaptive compensation framework that resolves temporal latency and noise interference in multi-agent systems
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论协同感知通过整合来自不同智能体的互补信息，显著增强了场景理解能力。然而，现有研究往往忽视了真实世界多源数据融合中固有的关键挑战，即时序高延迟与多源噪声。为应对这些实际限制，我们提出协同对齐与变换网络（CATNet），一种解决多智能体系统中时序延迟与噪声干扰的自适应补偿框架。我们的核心创新可归纳为三个方面。第一，我们引入时空循环同步模块（STSync），通过相邻帧差分建模对齐异步特征流，建立时序-空间统一的表示空间。第二，我们设计双分支小波增强去噪器（WTDen），在对齐后的表示中抑制全局噪声并重建局部特征畸变。第三，我们构建自适应特征选择器（AdpSel），动态聚焦关键感知特征以实现鲁棒融合。在多个数据集上的大量实验表明，CATNet 在复杂交通条件下持续优于现有方法，证明了其优越的鲁棒性与适应性。
英文原文摘要 1305 字符
Cooperative perception significantly enhances scene understanding by integrating complementary information from diverse agents. However, existing research often overlooks critical challenges inherent in real-world multi-source data integration, specifically high temporal latency and multi-source noise. To address these practical limitations, we propose Collaborative Alignment and Transformation Network (CATNet), an adaptive compensation framework that resolves temporal latency and noise interference in multi-agent systems. Our key innovations can be summarized in three aspects. First, we introduce a Spatio-Temporal Recurrent Synchronization (STSync) that aligns asynchronous feature streams via adjacent-frame differential modeling, establishing a temporal-spatially unified representation space. Second, we design a Dual-Branch Wavelet Enhanced Denoiser (WTDen) that suppresses global noise and reconstructs localized feature distortions within aligned representations. Third, we construct an Adaptive Feature Selector (AdpSel) that dynamically focuses on critical perceptual features for robust fusion. Extensive experiments on multiple datasets demonstrate that CATNet consistently outperforms existing methods under complex traffic conditions, proving its superior robustness and adaptability.