EvFlow-GS: Fusing Event Cameras and Optical Flow for Motion-Deblurred 3D Reconstruction

Motion Blur: A Persistent Challenge in 3D Reconstruction

In the field of computer vision, reconstructing high-quality 3D scenes from images captured by conventional cameras has long been a research hotspot. In recent years, 3D Gaussian Splatting (3DGS) has rapidly become the go-to approach for novel view synthesis, thanks to its exceptional rendering speed and visual quality. However, when input images suffer from motion blur, reconstruction quality degrades dramatically — blurry inputs directly produce blurry 3D models. This is particularly problematic in fast-motion scenarios such as robotic navigation and autonomous driving.

A recent paper published on arXiv introduces a unified framework called "EvFlow-GS," which ingeniously fuses event camera data with optical flow estimation to offer a novel solution for high-quality 3D Gaussian Splatting reconstruction under motion-blur conditions.

Core Innovation: Deep Fusion of Event Streams and Optical Flow

Traditional methods have attempted to leverage event cameras to address motion blur. Event cameras are bio-inspired visual sensors capable of asynchronously capturing pixel-level brightness changes at microsecond-level temporal resolution, making them inherently suited for recording high-speed motion information. However, existing event-enhanced reconstruction methods still face two major bottlenecks:

• Inaccurate event double integral priors: Previous methods typically relied on double integration of event streams to estimate sharp frames, but this prior itself suffers from accumulated errors, easily generating misleading supervisory signals.
• Event noise and blurring: The inherent noise of event cameras and the sparsity of events in low-texture regions introduce ghosting artifacts and loss of texture detail in reconstruction results.

The core idea behind EvFlow-GS is to move beyond sole reliance on integration priors from event data, instead deeply combining event streams with optical flow estimation to build a more reliable motion modeling pipeline. Optical flow provides pixel-level dense motion field information, effectively compensating for the shortcomings of event data in low-texture regions. Meanwhile, the high temporal resolution of event streams provides precise temporal interpolation anchors for optical flow estimation — the two modalities form a complementary pair.

Technical Pipeline Analysis

From an architectural perspective, EvFlow-GS unifies the entire deblurring and reconstruction process within an end-to-end framework. The method leverages the temporal information of event streams to guide the decomposition of camera motion during exposure time, while employing optical flow constraints to ensure spatial consistency of motion trajectories. This dual-constraint mechanism makes the process of recovering sharp frames from blurred images significantly more robust.

Compared to previous event-enhanced 3DGS methods, EvFlow-GS demonstrates notable advantages in the following areas:

• More accurate motion modeling: The synergistic constraints from optical flow and event streams avoid systematic biases introduced by relying on a single prior.
• Sharper texture details: Reduced texture degradation caused by event noise.
• Fewer ghosting artifacts: More accurate temporal interpolation effectively suppresses ghost artifacts in reconstruction.

Research Significance and Application Prospects

The value of this research lies not only in improved technical metrics but also in revealing the enormous potential of multi-modal sensor fusion in 3D reconstruction. Event cameras and traditional frame-based cameras each have their strengths and weaknesses, and designing effective fusion strategies has been a core challenge in the field. EvFlow-GS provides a trinitarian solution paradigm of "events + optical flow + Gaussian Splatting," laying an important foundation for future research.

From an application standpoint, this technology holds broad promise in the following scenarios:

• Autonomous driving: Real-time 3D scene reconstruction during high-speed vehicle travel.
• Robotic navigation: Environmental perception and mapping during rapid motion.
• AR/VR content creation: High-quality scene capture during fast handheld device scanning.
• Sports broadcasting: Free-viewpoint replay of high-speed sporting events.

Outlook

As event camera hardware costs continue to decline and 3D Gaussian Splatting technology rapidly iterates, multi-modal fusion 3D reconstruction solutions are transitioning from the laboratory to real-world applications. The event stream and optical flow synergy strategy demonstrated by EvFlow-GS is poised to become a key component of next-generation robust 3D reconstruction systems. Looking ahead, extending this framework to larger-scale outdoor scenes and achieving real-time processing will be research directions worth watching.