MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection

TL;DR

MTevent is a novel dataset combining high-speed motion, long-range perception, and real-world object interactions using event and RGB cameras, aimed at advancing perception in high-speed robotics environments.

Contribution

The paper introduces MTevent, the first dataset to integrate high-speed motion, long-range perception, and real-world object interactions for event-based vision research.

Findings

Baseline 6D pose estimation with RGB achieves an Average Recall of 0.22.

Event cameras offer promising advantages for high-speed perception.

The dataset enables research in dynamic, real-world robotic scenarios.

Abstract

Mobile robots are reaching unprecedented speeds, with platforms like Unitree B2, and Fraunhofer O3dyn achieving maximum speeds between 5 and 10 m/s. However, effectively utilizing such speeds remains a challenge due to the limitations of RGB cameras, which suffer from motion blur and fail to provide real-time responsiveness. Event cameras, with their asynchronous operation, and low-latency sensing, offer a promising alternative for high-speed robotic perception. In this work, we introduce MTevent, a dataset designed for 6D pose estimation and moving object detection in highly dynamic environments with large detection distances. Our setup consists of a stereo-event camera and an RGB camera, capturing 75 scenes, each on average 16 seconds, and featuring 16 unique objects under challenging conditions such as extreme viewing angles, varying lighting, and occlusions. MTevent is the first dataset to combine high-speed motion, long-range perception, and real-world object interactions, making it a valuable resource for advancing event-based vision in robotics. To establish a baseline, we evaluate the task of 6D pose estimation using NVIDIA's FoundationPose on RGB images, achieving an Average Recall of 0.22 with ground-truth masks, highlighting the limitations of RGB-based approaches in such dynamic settings. With MTevent, we provide a novel resource to improve perception models and foster further research in high-speed robotic vision. The dataset is available for download https://huggingface.co/datasets/anas-gouda/MTevent