# Optical Flow-Guided 6DoF Object Pose Tracking with an Event Camera

arXiv: https://arxiv.org/abs/2512.21053 · HTML: https://mlsanity.com/paper/2512.21053/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow](https://mlsanity.com/paper/2003.05102/) (2020)
- [Globally-Optimal Contrast Maximisation for Event Cameras](https://mlsanity.com/paper/2206.05127/) (2022)
- [Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking](https://mlsanity.com/paper/2002.05583/) (2020)
- [1000x Faster Camera and Machine Vision with Ordinary Devices](https://mlsanity.com/paper/2201.09302/) (2022)
- [A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking](https://mlsanity.com/paper/1807.02087/) (2018)
- [End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking](https://mlsanity.com/paper/2002.05911/) (2020)
- [VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals](https://mlsanity.com/paper/2104.06789/) (2021)
- [Dynamic Event Camera Calibration](https://mlsanity.com/paper/2107.06749/) (2021)
- [Combining 3D Model Contour Energy and Keypoints for Object Tracking](https://mlsanity.com/paper/2002.01379/) (2020)

## Built on by (most cited first, 2 of 2)

- [Event6D: Event-based Novel Object 6D Pose Tracking](https://mlsanity.com/paper/2603.28045/) (2026)
- [Event-based Motion & Appearance Fusion for 6D Object Pose Tracking](https://mlsanity.com/paper/2603.08264/) (2026)

## Abstract (arXiv)

Object pose tracking is one of the pivotal technologies in multimedia, attracting ever-growing attention in recent years. Existing methods employing traditional cameras encounter numerous challenges such as motion blur, sensor noise, partial occlusion, and changing lighting conditions. The emerging bio-inspired sensors, particularly event cameras, possess advantages such as high dynamic range and low latency, which hold the potential to address the aforementioned challenges. In this work, we present an optical flow-guided 6DoF object pose tracking method with an event camera. A 2D-3D hybrid feature extraction strategy is firstly utilized to detect corners and edges from events and object models, which characterizes object motion precisely. Then, we search for the optical flow of corners by maximizing the event-associated probability within a spatio-temporal window, and establish the correlation between corners and edges guided by optical flow. Furthermore, by minimizing the distances between corners and edges, the 6DoF object pose is iteratively optimized to achieve continuous pose tracking. Experimental results of both simulated and real events demonstrate that our methods outperform event-based state-of-the-art methods in terms of both accuracy and robustness.