KCTF-Net: Kinematic Compensation Temporal Fusion for 4D Radar 3D Object Detection

Abstract

4D millimeter-wave (mmWave) radar enables all-weather 3D object detection with reliable Doppler sensing. However, its practical application is hindered by inherent sparsity and noise. While multi-frame accumulation densifies point clouds, it inevitably introduces motion-induced spatiotemporal misalignment and geometric distortion, degrading detection accuracy in dynamic environments. To address these challenges, we propose the Kinematic Compensation Temporal Fusion Network (KCTF-Net), a novel 3D detection framework. Specifically, the Dynamic Kinematic Compensation (DKC) module explicitly aligns dynamic points in physical space, rectifying the motion-induced “smearing” effect. Furthermore, the Temporal Pillar Flow Enhancement (TPFE) module captures latent inter-frame kinematics, adaptively suppressing noise and mitigating sparsity. Finally, the Motion-Guided Attention Fusion (MGAF) module synergistically integrates density-filtered geometric priors with temporal features to reconstruct precise object geometries. Extensive experiments on the View-of-Delft (VoD) and TJ4DRadSet benchmarks demonstrate that KCTF-Net achieves state-of-the-art performance. Notably, it yields a 3D mean average precision (mAP) of 57.73%.