E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras

• URL: http://arxiv.org/abs/2608.14027v1
• Date: Fri, 14 Aug 2026 07:18:53 GMT
• Title: E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras
• Abstract summary: This paper proposes textbfE-S2Feat, a spiking neural network framework for event-based local feature detection and description.<n> Experiments show that the proposed method significantly outperforms baseline methods such as SuperEvent in pose estimation accuracy.<n>It also achieves accuracy comparable to its artificial neural network counterpart while delivering an approximately 4.8-fold improvement in theoretical computational energy efficiency.
• Score: 19.315869840048133
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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