CATNet: Collaborative Alignment and Transformation Network for Cooperative Perception
This paper tackles the practical limitations of temporal latency and multi-source noise in multi-agent cooperative perception systems, which is a problem for autonomous driving and robotics.
This paper addresses the challenges of high temporal latency and multi-source noise in cooperative perception by proposing CATNet, an adaptive compensation framework. CATNet consistently outperforms existing methods under complex traffic conditions, demonstrating superior robustness and adaptability.
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.