The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth
This dataset solves the problem of standardized evaluation for multimotion estimation techniques in robotics and computer vision, though it is incremental as it builds on existing dataset efforts.
The paper introduces the Oxford Multimotion Dataset, which addresses the lack of high-quality datasets for estimating every motion in a scene by providing over 110 minutes of data with ground truth for multiple complex motions, including stereo and RGB-D camera images, IMU data, and Vicon trajectories.
Datasets advance research by posing challenging new problems and providing standardized methods of algorithm comparison. High-quality datasets exist for many important problems in robotics and computer vision, including egomotion estimation and motion/scene segmentation, but not for techniques that estimate every motion in a scene. Metric evaluation of these multimotion estimation techniques requires datasets consisting of multiple, complex motions that also contain ground truth for every moving body. The Oxford Multimotion Dataset provides a number of multimotion estimation problems of varying complexity. It includes both complex problems that challenge existing algorithms as well as a number of simpler problems to support development. These include observations from both static and dynamic sensors, a varying number of moving bodies, and a variety of different 3D motions. It also provides a number of experiments designed to isolate specific challenges of the multimotion problem, including rotation about the optical axis and occlusion. In total, the Oxford Multimotion Dataset contains over 110 minutes of multimotion data consisting of stereo and RGB-D camera images, IMU data, and Vicon ground-truth trajectories. The dataset culminates in a complex toy car segment representative of many challenging real-world scenarios. This paper describes each experiment with a focus on its relevance to the multimotion estimation problem.