RainDancer Uses RGB-Event Fusion and Spiking Dynamics for Video Deraining

Lead

Video deraining is more than a cosmetic enhancement task. In outdoor perception systems such as autonomous driving, robotics, and surveillance, rain streaks can obscure structure, distort motion cues, and weaken downstream recognition. Most existing methods rely mainly on RGB video and temporal redundancy, but rainy dynamic scenes are highly ambiguous: rain streaks, textures, object boundaries, motion blur, and occlusions can look deceptively similar.

The arXiv paper “RainDancer: RGB-Event Video Deraining with Rain-Oriented Spiking Dynamics” proposes a different route. It brings event cameras into the deraining pipeline. Unlike conventional frame cameras, event cameras asynchronously report brightness changes and offer very high temporal resolution. This makes them potentially useful for capturing fast, sparse, and burst-like motion patterns associated with rain.

Key ideas

• Fusion is not automatically beneficial. The paper stresses that event streams are not clean labels for motion. They may include sensor noise and responses triggered by background changes. Direct RGB-Event fusion can therefore introduce cross-modal interference instead of solving the ambiguity.
• Decompose before interact. RainDancer adopts a progressive framework in which each modality is first decomposed into rain and background components. In the RGB branch, frame features are gradually separated into rain-related and background representations.
• Rain-oriented spiking dynamics. In the event branch, the model uses a rain-oriented spiking neural network module to capture sparse and bursty event patterns tied to rain motion. This is a natural fit for event data, which is temporal, asynchronous, and sparse by design.
• Component-level fusion. Rather than mixing all features together, RainDancer fuses semantically aligned components. Rain-related information can help suppress streaks, while background-related information supports structural preservation.
• Event-domain supervision. The framework further introduces supervision for sparse event reconstruction, structural consistency, and gradient orientation, aiming to regularize learning in the event domain and reduce harmful noise.

Why it matters

RainDancer is interesting because it treats event cameras as more than an auxiliary signal. The method recognizes both their strength and their risk: events can reveal fast rain dynamics, but they can also carry unwanted responses. By decomposing signals before interaction, the framework tries to make cross-modal collaboration more selective and interpretable.

According to the paper, experiments on synthetic and real RGB-Event video deraining datasets show improved quantitative performance, visual quality, and downstream perception robustness. That matters because the real value of deraining is not only a cleaner frame, but a more reliable perception stack under adverse weather.

The provided material does not include exact metrics, dataset details, or runtime cost, so deployment feasibility still requires reading the full paper. Even so, RainDancer offers a notable architectural direction for multimodal low-level vision: align what each modality means before fusing what each modality sees.

Source: arXiv