CoSTFE: Spatio-Temporal Feature Enhancement for Collaborative Perception
Meiling Wang, Xunjie He, Yiming Li, Yufeng Yue · IEEE Transactions on Intelligent Transportation Systems · 2025
Collaborative perception enables a more comprehensive and precise representation of the environment, owing to the complementary information shared among different agents. However, spatio-temporal disturbances, including localization errors (spatial) and time delays (temporal), are prevalent in practical applications and significantly impair detection performance. To improve both the accuracy and robustness, a novel framework called Spatio-Temporal Feature Enhancement for Collaborative Perception (CoSTFE) is proposed. Specifically, we present a Histogram-based Spatial Correction (HSC) module to optimize the transformation matrix and promote the robustness when localization errors happen. In addition, the Deformable Temporal Augmentation (DTA) module is introduced to predict and enhance the current characteristic with long-term historical dynamics. Compared with existing methods on three publicly available collaborative perception datasets, our approach exhibits superior performance and robustness in the collaborative 3D object detection task.