MECI: A Multi-Model Motion Capture-Free Event Dataset Featuring Large-Scale Challenging Indoor Environments

Recently, several event-based datasets have emerged to foster the application of the new event camera to classic vision tasks like SLAM (Simultaneous Localization And Mapping). However, current indoor benchmark datasets depend on the expensive motion capture system to obtain ground-truth trajectories, restricting data acquisition to small object-centric scenes or single-room environments due to infrastructure costs and spatial limitations. Furthermore, these datasets lack sensor diversity, relying solely on a single event camera model that hinders practical cross-device generalization. To address the above limitations, we propose MECI, the first Multi-model Event dataset targeting Challenging Indoor environments, especially including large-scale scenes with complete trajectory ground truth provided. Specifically, MECI includes 38 posed sequences of visual-inertial-event data from three different models of event cameras with varying resolutions and data frequencies. Apart from typical challenging factors such as illumination changes, motion blur, and dynamics, these sequences involve large-scale indoor scenes (across rooms and floors) with each room occupying approximately 60m \({}²\) and a maximum trajectory length of 343.8m. We innovatively leverage ETS (Electronic Total Station) measurements and AprilTag markers to provide complete 6-DoF trajectories with low cost and minimal environmental modifications. Extensive experiments show that our MECI is an effective yet challenging benchmark dataset not only for visual SLAM, but also for event-image reconstruction. Our project page is https://cslinzhang.github.io/MECI_dataset/ .