See Silhouettes in Motion with Neuromorphic Vision

2026-05-18

AI Summary

This work addresses the challenge of generating sharp binary contours in dynamic scenes, where conventional frame-based imaging suffers from motion blur and extreme lighting conditions. The authors propose a dual-modality approach that fuses event camera data with conventional frame images through an asynchronous coordination mechanism. This design circumvents the sparsity issues caused by temporal binning of events and enables real-time, kilohertz-rate binarization on standard CPUs. The method substantially outperforms existing techniques, delivering high-quality contours even under severe motion blur and low-light scenarios. It effectively supports a variety of downstream vision tasks and is well-suited for deployment on resource-constrained edge computing platforms.

Abstract

Quasi-bimodal objects, such as text, road signs, and barcodes, play a basic yet vital role in daily visual communication. By boiling these down to clear silhouettes, binarization uses a minimal language to convey essential vision cues for maximum downstream efficiency. The catch is that frame-based imaging often struggles on mobile platforms like drones, self-driving cars, and underwater vehicles. In these dynamic scenes, rapid motion and harsh lighting can make it blind, causing severe motion blur and erasing crucial details. To overcome the limits, neuromorphic vision via event cameras, featuring microsecond-level temporal resolution and high dynamic range, steps in as a natural solution. Building upon this event-driven sensing paradigm, we introduce a simple yet effective dual-modal approach that harnesses the synergy between frames and events to achieve real-time, high-frame-rate binarization on CPU-only devices. Extensive evaluations present that it earns competitive performance against leading techniques in reducing motion blur, while delivering impressive improvements under challenging illumination. Besides, our asynchronous workflow bypasses event scarcity that breaks traditional time-binning reconstruction, maintaining clear target shapes even at extreme kilohertz frame rates. Its binary results further serve as reliable representations that facilitate a range of downstream tasks. This work paves the way towards lightweight perception and interaction in embodied intelligence on resource-constrained edge platforms.

Problem

• motion blur
• binarization
• event cameras
• dynamic scenes
• frame-based imaging

Innovation

• neuromorphic vision
• event cameras
• real-time binarization
• dual-modal fusion
• motion blur reduction