发表机构
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出E-S2Feat脉冲神经网络框架，解决事件局部特征学习的稀疏性、噪声等问题，在多数据集实验中精度优于基线方法，能效提升约4.8倍，可应用于SLAM系统。
AI 中文摘要
基于事件的局部特征方法因具备高时间分辨率和动态范围而受到越来越多关注，但事件的稀疏性、噪声以及有限的纹理仍阻碍了鲁棒局部特征的学习；将这类方法部署在无人机等资源受限平台时，还需平衡精度与能效。为应对这些挑战，本文提出E-S2Feat，这是一种用于事件局部特征检测与描述的脉冲神经网络框架，该框架从特征表示与选择的角度联合优化局部特征学习：其一，模块专用的脉冲激活机制在低比特、高能效推理下保留细粒度结构线索与判别信息，提升整体表示保真度；其二，语义引导的特征调制机制利用语义先验优化关键点响应分布，增强局部描述子的判别能力，引导模型提取几何稳定性更高、判别性更强的局部特征。在ECD与EDS数据集上的实验表明，该方法在位姿估计精度上显著优于SuperEvent等基线方法，且精度可与对应的人工神经网络方法媲美，理论计算能效提升约4.8倍；在TUM-VIE数据集上的视觉-惯性里程计实验进一步验证了该方法在完整SLAM系统中的有效性与实际应用潜力。
英文摘要
Benefiting from high temporal resolution and dynamic range, event-based local feature methods have attracted increasing attention. However, event sparsity, noise, and limited texture still hinder robust local feature learning. Deploying such methods on resource-constrained platforms such as unmanned aerial vehicles also requires balancing accuracy and energy efficiency. To address these challenges, this paper proposes \textbf{E-S2Feat}, a spiking neural network framework for event-based local feature detection and description. The framework jointly optimizes local feature learning from the perspectives of feature representation and selection. First, a module-specific spiking activation mechanism preserves fine-grained structural cues and discriminative information under low-bit, energy-efficient inference, thereby improving overall representation fidelity. Furthermore, a semantic-guided feature modulation mechanism leverages semantic priors to refine keypoint response distributions and enhance local descriptor discriminability, thereby guiding the model to extract local features with greater geometric stability and stronger discriminative capability. Experiments on the ECD and EDS datasets show that the proposed method significantly outperforms baseline methods such as SuperEvent in pose estimation accuracy. It also achieves accuracy comparable to its artificial neural network counterpart while delivering an approximately 4.8-fold improvement in theoretical computational energy efficiency. Visual-inertial odometry experiments on the TUM-VIE dataset further verify the effectiveness and practical application potential of the proposed method in complete SLAM systems.
