Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera

TL;DR

This paper introduces a line-based method for 6-DoF object pose estimation and tracking using event cameras, leveraging their high dynamic range and low latency to improve robustness and accuracy in challenging scenes.

Contribution

It presents a novel approach that extracts object lines from events, uses a global optimization for initial pose, and refines tracking through event-line matching and robust estimation.

Findings

Outperforms state-of-the-art methods in robustness and accuracy.

Validated on synthetic and real event-based datasets.

Provides a new dataset for event-based object pose estimation.

Abstract

Pose estimation and tracking of objects is a fundamental application in 3D vision. Event cameras possess remarkable attributes such as high dynamic range, low latency, and resilience against motion blur, which enables them to address challenging high dynamic range scenes or high-speed motion. These features make event cameras an ideal complement over standard cameras for object pose estimation. In this work, we propose a line-based robust pose estimation and tracking method for planar or non-planar objects using an event camera. Firstly, we extract object lines directly from events, then provide an initial pose using a globally-optimal Branch-and-Bound approach, where 2D-3D line correspondences are not known in advance. Subsequently, we utilize event-line matching to establish correspondences between 2D events and 3D models. Furthermore, object poses are refined and continuously tracked by minimizing event-line distances. Events are assigned different weights based on these distances, employing robust estimation algorithms. To evaluate the precision of the proposed methods in object pose estimation and tracking, we have devised and established an event-based moving object dataset. Compared against state-of-the-art methods, the robustness and accuracy of our methods have been validated both on synthetic experiments and the proposed dataset. The source code is available at https://github.com/Zibin6/LOPET.