# The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data arXiv: https://arxiv.org/abs/2106.06887 ยท HTML: https://mlsanity.com/paper/2106.06887/ ## Key facts - Cited by 27 papers in the map (data through 14 Oct 2026). - Builds on 12 of its 35 references that are in the map. - First posted on arXiv 13 Jun 2021. ## Builds on (most cited first, 12 of 12) - [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019) - [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019) - [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018) - [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016) - [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018) - [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017) - [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018) - [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019) - [Event-Based Motion Segmentation by Motion Compensation](https://mlsanity.com/paper/1904.01293/) (2019) - [Globally Optimal Contrast Maximisation for Event-based Motion Estimation](https://mlsanity.com/paper/2002.10686/) (2020) - [Real-Time Panoramic Tracking for Event Cameras](https://mlsanity.com/paper/1703.05161/) (2017) - [Nonparametric Curve Alignment](https://mlsanity.com/paper/1902.00626/) (2019) ## Built on by (most cited first, 27 of 27) - [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022) - [Ev-NeRF: Event Based Neural Radiance Field](https://mlsanity.com/paper/2206.12455/) (2022) - [Event Collapse in Contrast Maximization Frameworks](https://mlsanity.com/paper/2207.04007/) (2022) - [CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization](https://mlsanity.com/paper/2403.08119/) (2024) - [Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://mlsanity.com/paper/2112.06242/) (2021) - [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023) - [A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework](https://mlsanity.com/paper/2212.07350/) (2022) - [Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion](https://mlsanity.com/paper/2311.00436/) (2023) - [Implicit Event-RGBD Neural SLAM](https://mlsanity.com/paper/2311.11013/) (2023) - [Motion and Structure from Event-based Normal Flow](https://mlsanity.com/paper/2407.12239/) (2024) - [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022) - [Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers](https://mlsanity.com/paper/2503.03307/) (2025) - [E3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2408.01840/) (2024) - [Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios](https://mlsanity.com/paper/2508.10704/) (2025) - [Event-based Mosaicing Bundle Adjustment](https://mlsanity.com/paper/2409.07365/) (2024) - [Simultaneous Motion And Noise Estimation with Event Cameras](https://mlsanity.com/paper/2504.04029/) (2025) - [Density Invariant Contrast Maximization for Neuromorphic Earth Observations](https://mlsanity.com/paper/2304.14125/) (2023) - [Event-based Photometric Bundle Adjustment](https://mlsanity.com/paper/2412.14111/) (2024) - [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025) - [Iterative Event-based Motion Segmentation by Variational Contrast Maximization](https://mlsanity.com/paper/2504.18447/) (2025) - [Active Event Alignment for Monocular Distance Estimation](https://mlsanity.com/paper/2410.22280/) (2024) - [E-VLC: A Real-World Dataset for Event-based Visible Light Communication And Localization](https://mlsanity.com/paper/2504.18521/) (2025) - [Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking](https://mlsanity.com/paper/2503.08370/) (2025) - [Event Stream Filtering via Probability Flux Estimation](https://mlsanity.com/paper/2504.07503/) (2025) - [Geometric-Photometric Event-based 3D Gaussian Ray Tracing](https://mlsanity.com/paper/2512.18640/) (2025) - [Unbiased Gradient Estimation for Event Binning via Functional Backpropagation](https://mlsanity.com/paper/2602.12590/) (2026) - [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026) ## Abstract (arXiv) Event cameras, inspired by biological vision systems, provide a natural and data efficient representation of visual information. Visual information is acquired in the form of events that are triggered by local brightness changes. Each pixel location of the camera's sensor records events asynchronously and independently with very high temporal resolution. However, because most brightness changes are triggered by relative motion of the camera and the scene, the events recorded at a single sensor location seldom correspond to the same world point. To extract meaningful information from event cameras, it is helpful to register events that were triggered by the same underlying world point. In this work we propose a new model of event data that captures its natural spatio-temporal structure. We start by developing a model for aligned event data. That is, we develop a model for the data as though it has been perfectly registered already. In particular, we model the aligned data as a spatio-temporal Poisson point process. Based on this model, we develop a maximum likelihood approach to registering events that are not yet aligned. That is, we find transformations of the observed events that make them as likely as possible under our model. In particular we extract the camera rotation that leads to the best event alignment. We show new state of the art accuracy for rotational velocity estimation on the DAVIS 240C dataset. In addition, our method is also faster and has lower computational complexity than several competing methods.