Moving6DPoSe: A Multimodal Database for Monocular 6D Pose Estimation and Segmentation of Moving Objects

Estimating the 6D pose of moving objects remains challenging due to motion blur and the limited temporal resolution of conventional frame-based cameras. Existing event-based datasets further provide limited sensing modalities, annotations, and motion scenarios. We introduce Moving6DPoSe, a multimodal database comprising two complementary subsets: Moving6DPoSe-R with real-world recordings and Moving6DPoSe-S with synthetic sequences generated from the same objects. The dataset contains 16 scanned objects and 1,702 real and synthetic rosbags spanning multiple motion scenarios, with annotations for semantic segmentation, object detection, and monocular 6D pose estimation. We further provide baseline results for all three tasks across frame and event-based modalities. Experimental results show that event-based representations achieve more robust moving-object segmentation than conventional RGB images, while monocular orientation estimation remains challenging, highlighting the potential of Moving6DPoSe for moving-object perception research.