Coupling and Decoupling: Towards Temporal Feedback for 3D Object Detection

3D object detection has garnered significant attention within the academic community, primarily due to its broad utility in domains such as autonomous driving and robotics. Prior research efforts have predominantly concentrated on leveraging temporal contextual information embedded within sequential data to enhance the current feature representations. However, a notable limitation of these endeavors lies in their inadequate treatment of the inherent noise present within historical sequences, thereby constraining the efficiency of fusion methods. In this paper, we propose a new temporal feedback network, named TFNet, to model and correct the temporal noise by designing a coupling-decoupling mechanism. Central to our approach are two distinct modules: (i) Foreground Feature Enhancement, which amplifies sparse instance details across temporal frames, thereby furnishing essential local information priors for subsequent fusion; and (ii) Coupling-Decoupling Feature Interaction, designed to first aggregate temporal contextual information and then disentangle fusion features into frame-specific representations. Leveraging a feedback strategy, this module can adaptively enhance useful information and eliminate noise within individual frame features. Empirical evaluations conducted on the nuScenes benchmark demonstrate the effectiveness of TFNet, achieving the new state-of-the-art performance without any bells and whistles.