[Paper] EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregation

Source: arXiv - 2511.21439v1

Overview

Event cameras capture changes in illumination as a continuous stream of asynchronous “events” rather than traditional frame‑based images. While this yields ultra‑low latency and high dynamic range, the resulting data are extremely sparse in space, making it hard for neural networks to learn robust representations. The paper “EvRainDrop: HyperGraph‑guided Completion for Effective Frame and Event Stream Aggregation” introduces a hypergraph‑based completion module that fills in missing event information and seamlessly fuses it with RGB data, dramatically improving classification performance on both single‑ and multi‑label tasks.

Key Contributions

• Hypergraph‑guided spatio‑temporal completion: Connects event tokens across time and space via hyperedges, allowing contextual message passing that “fills in” undersampled regions.
• Multi‑modal integration: Treats RGB patches as additional nodes in the same hypergraph, enabling joint completion of event and frame data without separate pipelines.
• Self‑attention aggregation: After completion, node features from all time steps are aggregated with a transformer‑style self‑attention block, yielding a compact yet expressive representation.
• State‑of‑the‑art results: Sets new benchmarks on several event‑camera classification datasets (e.g., N‑Caltech101, N‑CARS) for both single‑label and multi‑label settings.
• Open‑source implementation: Code and pretrained models will be released, facilitating reproducibility and downstream research.

Methodology

1. Event Tokenization – The raw event stream is first partitioned into short temporal windows (e.g., 10 ms). Within each window, events are rasterized into a sparse 2‑D map and then embedded into a set of event tokens using a lightweight CNN.
2. Hypergraph Construction –
   • Nodes: Event tokens from each window plus optional RGB tokens (if a conventional frame is available).
   • Hyperedges: Each hyperedge links a group of nodes that are close in space or time, capturing long‑range dependencies that ordinary graphs miss.
3. Message Passing & Completion – A hypergraph neural network (HGNN) iteratively exchanges information across hyperedges. Because each hyperedge aggregates many nodes, the network can infer missing event activity from surrounding context, effectively “completing” the sparse stream.
4. Temporal Fusion via Self‑Attention – Completed node embeddings from all windows are fed into a transformer‑style self‑attention module. This learns how to weight different time steps and modalities, producing a single feature vector per video clip.
5. Classification Head – The fused representation is passed through a linear classifier (single‑label) or a sigmoid‑based multilabel head, trained with cross‑entropy or binary‑cross‑entropy loss respectively.

The whole pipeline is end‑to‑end differentiable, so the hypergraph structure can be learned jointly with the downstream task.

Results & Findings

• Ablation studies show that removing hypergraph completion drops accuracy by ~3–5 %, confirming its central role.
• Adding RGB nodes improves performance on datasets where synchronized frames are available, but the method still outperforms frame‑only baselines even without RGB.
• The hypergraph module adds modest computational overhead (~15 % extra FLOPs) while keeping inference latency well under 30 ms on a modern GPU, preserving the low‑latency advantage of event cameras.

Practical Implications

• Robotics & Drones: Real‑time perception in high‑speed or high‑dynamic‑range environments (e.g., fast‑moving drones) can now rely on richer event representations without sacrificing latency.
• AR/VR Headsets: Event sensors can be paired with conventional RGB cameras to deliver low‑latency gesture or eye‑tracking, with the hypergraph completing missing event data caused by rapid head motions.
• Edge AI Devices: The lightweight completion module can be deployed on embedded GPUs or NPUs, enabling on‑device inference for autonomous vehicles, surveillance cameras, or industrial inspection systems that need to operate under extreme lighting.
• Multi‑modal Fusion Research: By treating RGB patches as hypergraph nodes, the approach offers a generic recipe for fusing any asynchronous sensor (LiDAR, radar) with event streams, opening doors to more robust sensor‑fusion pipelines.

Limitations & Future Work

• Scalability to Very Long Sequences: The current hypergraph is built over a fixed number of temporal windows; extremely long recordings may require hierarchical or sliding‑window hypergraphs to keep memory usage bounded.
• Dependence on Synchronized RGB (optional): While the method works without RGB, the biggest gains come when both modalities are present, which may limit applicability in pure event‑only setups.
• Hyperparameter Sensitivity: The size of hyperedges (how many nodes they connect) influences performance; automated learning of edge formation could make the system more plug‑and‑play.
• Future Directions: The authors suggest exploring dynamic hypergraph construction via attention, extending the framework to event‑based object detection/segmentation, and optimizing the module for ultra‑low‑power ASICs.

EvRainDrop demonstrates that a clever graph‑theoretic view of sparse event data can bridge the gap between the theoretical advantages of event cameras and the practical needs of developers building real‑world perception systems. With the upcoming open‑source rel