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E-POSE: A Large Scale Event Camera Dataset for Object Pose Estimation.

Oussama Abdul Hay¹, Xiaoqian Huang¹, Abdulla Ayyad¹

¹Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.

Scientific Data

|February 12, 2025

Summary

This summary is machine-generated.

This study introduces the largest event-based dataset for 6D object pose estimation, crucial for robotic grasping. The new dataset, featuring Yale-CMU-Berkeley objects, will advance robotic manipulation research.

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Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

Robotic automation relies on precise 6D object pose estimation for manipulation.
Event-based cameras offer advantages over conventional cameras but lack dedicated datasets for pose estimation.

Purpose of the Study:

To introduce a comprehensive dataset for event-based 6D object pose estimation.
To facilitate research and development of novel pose estimation algorithms for robotics.

Main Methods:

Developed an extensive dataset using Yale-CMU-Berkeley (YCB) objects.
Captured event packets with associated ground truth poses, spike images, masks, and 3D bounding boxes.
Included diverse scenarios with varying clutter, object types, speeds, and illumination conditions.

Main Results:

The dataset comprises 306 sequences, over an hour of data, and 1.5 billion events.
Features 13 YCB objects across 18 scenarios, covering cluttered and uncluttered scenes.
Provides segmented events and a 3-channel event image for robust validation.

Conclusions:

This dataset is the largest and most diverse resource for event-based 6D object pose estimation.
It addresses the critical need for data to advance robotic manipulation and grasping.
Aims to accelerate the development and testing of advanced object pose estimation algorithms.