# EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception

arXiv: https://arxiv.org/abs/2504.16616 · HTML: https://mlsanity.com/paper/2504.16616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 104 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 30 of 56)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [A Large Scale Event-based Detection Dataset for Automotive](https://mlsanity.com/paper/2001.08499/) (2020)
- [Event-based Asynchronous Sparse Convolutional Networks](https://mlsanity.com/paper/2003.09148/) (2020)
- [Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding](https://mlsanity.com/paper/2105.12723/) (2021)
- [Graph-Based Object Classification for Neuromorphic Vision Sensing](https://mlsanity.com/paper/1908.06648/) (2019)
- [Object Detection with Spiking Neural Networks on Automotive Event Data](https://mlsanity.com/paper/2205.04339/) (2022)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [Hyperbolic Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/2305.06611/) (2023)
- [GET: Group Event Transformer for Event-Based Vision](https://mlsanity.com/paper/2310.02642/) (2023)
- [Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification](https://mlsanity.com/paper/2104.14913/) (2021)
- [Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras](https://mlsanity.com/paper/1805.07931/) (2018)
- [Graph-based Asynchronous Event Processing for Rapid Object Recognition](https://mlsanity.com/paper/2308.14419/) (2023)
- [A Voxel Graph CNN for Object Classification with Event Cameras](https://mlsanity.com/paper/2106.00216/) (2021)
- [Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation](https://mlsanity.com/paper/2108.06468/) (2021)
- [Exploring Event Camera-based Odometry for Planetary Robots](https://mlsanity.com/paper/2204.05880/) (2022)
- [Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras](https://mlsanity.com/paper/2211.12324/) (2022)
- [Knowledge Association with Hyperbolic Knowledge Graph Embeddings](https://mlsanity.com/paper/2010.02162/) (2020)

## Built on by (most cited first, 1 of 1)

- [Low-Latency Event-Based Object Detection with Spatially-Sparse Linear Attention](https://mlsanity.com/paper/2603.06228/) (2026)

## Abstract (arXiv)

Event cameras, characterized by microsecond temporal resolution and very High Dynamic Range (HDR), emit high-speed event streams for perception tasks. In recent advancements, Graph Neural Networks (GNNs)-based methods show great potential in event perception. However, they typically rely on straightforward pairwise node connectivity in Euclidean space where they struggle to capture long-range dependencies and faithfully characterize the inherent hierarchical structures of event streams. To this end, we propose EHGCN, a dual-space event perception approach that, to the best of our knowledge, is the first to jointly model event streams in Euclidean and hyperbolic spaces. By introducing hyperbolic geometry into event stream perception, EHGCN enables to naturally capture the anisotropic and hierarchical structures of non-uniform, motion-driven event streams. Specifically, we first introduce a distribution-aware event sifting method based on multi-scale voxel grids and Gaussian distribution modeling, retaining discriminative events while attenuating chaotic noise. Then, we present a Markov Random Field (MRF)-optimized motion-aware hyperedge generation scheme, which minimizes a motion consistency energy function to explicitly capture consistent global motion patterns within short time intervals, thereby eliminating cross-target spurious associations and providing critically topological priors while capturing long-range dependencies among events. Finally, we propose a Euclidean-hyperbolic GCN to fuse the retinal events densely aggregated and hierarchically modeled in local Euclidean and global hyperbolic spaces, respectively, to achieve a hybrid event perception. Extensive experimental results on event perception tasks, such as object detection and recognition, show the effectiveness of our approach. Our code will be released for public use at https://github.com/ev-lluo/EHGCN.