STRDet: Robust 3D object detection for autonomous driving via spatio-temporal feature refinement
TL;DR
STRDet, a robust 3D object detection framework based on progressive spatio-temporal feature refinement, is proposed and a Semantic-guided Context Refinement (SCR) module that explicitly suppresses background interference prior to the view transformation, thereby blocking noise propagation is proposed.
Abstract
Camera-based 3D object detection has attracted widespread attention for autonomous driving applications. However, existing methods often lack effective feature screening mechanisms, resulting in an extremely low spatio-temporal signal-to-noise ratio in complex scenes. Specifically, distracting background projections, feature misalignment caused by dynamic objects, and frequent occlusions jointly lead to severe ambiguity and loss of object features. To alleviate these issues, we propose STRDet, a robust 3D object detection framework based on progressive spatio-temporal feature refinement. First, we propose a Semantic-guided Context Refinement (SCR) module that explicitly suppresses background interference prior to the view transformation, thereby blocking noise propagation. Second, we design the Differential-aware Feature Alignment (DFA) and Residual-based Adaptive Gated Fusion (RAGF) modules, which leverage feature difference maps as motion saliency indicators to guide deformable alignment and employ gating mechanisms to selectively integrate historical motion cues, effectively resolving dynamic alignment failures and mitigating feature loss under occlusion. Extensive experiments on the nuScenes dataset demonstrate that STRDet effectively enhances feature purity and coherence, yielding significant improvements and achieving 46.95\% mAP and 55.37\% nuScenes detection score.