Tracking 6-DoF Object Motion from Events and Frames

TL;DR

This paper introduces a novel method for 6-DoF object motion tracking that integrates event camera data with traditional frame-based images, achieving high accuracy in various scenarios.

Contribution

The approach combines probabilistic modeling of event data with direct image alignment, enabling improved 6-DoF tracking from asynchronous event and frame data.

Findings

Accurate tracking demonstrated on synthetic data

Effective real-data experiments validate the method

Combines high-rate event data with slower frame updates

Abstract

Event cameras are promising devices for lowlatency tracking and high-dynamic range imaging. In this paper,we propose a novel approach for 6 degree-of-freedom (6-DoF)object motion tracking that combines measurements of eventand frame-based cameras. We formulate tracking from highrate events with a probabilistic generative model of the eventmeasurement process of the object. On a second layer, we refinethe object trajectory in slower rate image frames through directimage alignment. We evaluate the accuracy of our approach inseveral object tracking scenarios with synthetic data, and alsoperform experiments with real data.