Spiking-Refined 3D Object Detection through YOLO–SNN Fusion

Abstract

This paper presents Spiking-Refined 3D Object Detection through YOLO–SNN Fusion, a real-time pipeline that leverages both convolutional and spiking neural representations for enhanced scene perception. Our system integrates YOLOv11 for robust 2D detection, Depth Anything v2 for monocular depth inference, and geometry-based reasoning for 3D bounding box construction, while a Bird’s-Eye View visualizer provides spatial context. To further improve recognition consistency, we fuse the predictions of a trained Spiking Neural Network (SNN) with YOLO outputs, enabling class refinement that is more resilient to temporal noise and ambiguous appearances. Kalman filtering is employed to stabilize trajectories over time, ensuring coherent 3D tracking. Unlike sensor-heavy setups, our approach runs on a single RGB camera and lightweight models, making it suitable for robotic perception, AR/VR applications, and low-cost embedded platforms. Experiments on real-world video sequences demonstrate improved 3D detection accuracy, temporal stability, and cross-class discrimination compared to conventional monocular pipelines.