发表机构
Hunan University; National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University; Nanjing University of Posts and Telecommunications（湖南大学; 湖南大学机器人视觉感知与控制技术国家工程研究中心; 南京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EgoRefine提出自我参考预测对齐与轨迹条件可靠性感知融合，解决异步协同感知中的时间延迟与错位问题，在V2V4Real和DAIR-V2X-Seq上分别提升AP@0.5和AP@0.7达1.6和2.9个百分点。
AI 中文摘要
协同感知使互联智能体能够共享互补的观测信息以进行3D物体检测，从而扩展感知范围并缓解遮挡问题。然而，在异步通信下，协同特征会带有时间延迟到达。现有的基于预测的方法主要从发送智能体自身的历史信息中补偿这些特征，导致与自我智能体当前观测之间仍存在残余错位；后续的融合也常常忽略对齐质量的空间变化。我们提出了EgoRefine，一个面向异步协同感知的自我参考预测对齐与可靠性感知融合框架。其自我参考预测对齐模块利用当前自我特征来引导协同轨迹场预测，并沿自我参考轨迹方向细化采样偏移。其轨迹条件可靠性感知融合模块将自我与协同流之间的轨迹差异以及方向细化幅度视为对齐线索，利用它们来调节对齐特征之间的关系，并在卷积融合前自适应地重新加权两条流。在V2V4Real和DAIR-V2X-Seq上的实验表明，EgoRefine在AP@0.5和AP@0.7上分别平均比TraF-Align高出1.6和2.9个百分点。源代码将在该https URL上公开提供。
英文摘要
Collaborative perception enables connected agents to share complementary observations for 3D object detection, extending sensing range and mitigating occlusion. Under asynchronous communication, however, cooperative features arrive with temporal delay. Existing prediction-based methods compensate for these features mainly from the transmitting agent's own history, leaving residual misalignment with the ego agent's current observation; subsequent fusion also often overlooks spatial variations in alignment quality. We propose EgoRefine, an ego-referenced predictive alignment and reliability-aware fusion framework for asynchronous collaborative perception. Its Ego-referenced Predictive Alignment module uses the current ego feature to guide cooperative trajectory-field prediction and refines the sampling offsets along an ego-referenced trajectory direction. Its Trajectory-conditioned Reliability-aware Fusion module treats the trajectory discrepancy between the ego and cooperative streams and the directional refinement magnitude as alignment cues, using them to condition the relation between aligned features and adaptively reweight the two streams before convolutional fusion. Experiments on V2V4Real and DAIR-V2X-Seq show that EgoRefine outperforms TraF-Align by 1.6 and 2.9 points on average in AP@0.5 and AP@0.7, respectively. The source code will be made publicly available at https://github.com/godk0509/EgoRefine.
CommentsThe source code will be made publicly available at https://github.com/godk0509/EgoRefine
