FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

FS-DVS, a Frequency-Selective Dynamic Vision Sensor, introduces a novel paradigm to overcome the information incompleteness and noise susceptibility of conventional dynamic vision sensors (DVS). It integrates a learnable spatial filter, optimized end-to-end via a differentiable event simulation framework, strictly prior to the event triggering process. This design mimics the spatial aggregation mechanism of biological retinal ganglion cells (RGCs). The study demonstrates that these learned spatial filters spontaneously evolve into center-surround patterns, emphasizing mid-spatial frequencies, which consistently aligns with the human Contrast Sensitivity Function (CSF). FS-DVS achieves substantial performance gains, including a +12.3% mAP in simulated object detection and +10.8 mAP in physical validation, along with +8.86% accuracy in simulated action recognition and +6.42% in physical tests. It also shows +4.77% mIoU improvement in zero-shot semantic segmentation, proving its robustness and transferability.

Key takeaway

For AI/Computer Vision Engineers developing next-generation neuromorphic sensors, FS-DVS offers a robust blueprint to overcome current DVS limitations. You should consider integrating a learnable, pre-trigger spatial filter into your event camera designs to enhance structural completeness and noise resilience. This approach, validated with significant performance gains in detection and recognition, provides a biologically plausible and transferable mechanism for improving event data quality, potentially via compact ASIC or optical implementations.

Key insights

FS-DVS uses a learnable pre-trigger spatial filter to mimic RGCs, enhancing event camera data completeness and noise resilience.

Principles

• Pre-trigger spatial filtering improves event data quality.
• Task-driven optimization yields biologically plausible sensing.
• Mid-frequency emphasis is optimal for robust perception.

Method

A differentiable event simulation framework allows end-to-end optimization of a spatial convolution kernel (e.g., 7x7) placed before event triggering, using downstream task losses.

In practice

• Implement a learnable spatial filter in event camera pipelines.
• Utilize sensor-in-the-loop validation for real-world robustness.
• Consider dual-resistive-mesh or ASIC for hardware integration.

Topics

• Dynamic Vision Sensors
• Neuromorphic Sensing
• Spatial Filtering
• Event Cameras
• Object Detection
• Action Recognition
• Contrast Sensitivity Function

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Robotics Engineer

Related on AIssential

• FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness — FS-DVS uses a learnable spatial filter to mimic biological vision, enhancing DVS information completeness and performance.
• NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research — NERVE is a multi-sensor dataset bridging neuromorphic vision and radar for robust perception research.
• SNNF: An SNN-based Near-Sensor Noise Filter for Dynamic Vision Sensors — SNNF efficiently filters DVS noise using a compact binary representation and a hardware-optimized Spiking Neural Network.
• Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras — A new event matching model achieves zero-shot wide-baseline correspondence by combining efficient architecture and diverse synthetic data.
• FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views — FRUC enables efficient, calibration-free 3D dynamic scene reconstruction from uncalibrated collaborative driving views using a feed-forward approach.
• Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain — Frequency domain processing of radar data enhances robustness and inherently detects all moving objects for multi-object tracking.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.