# The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth

arXiv: https://arxiv.org/abs/1901.01445 · HTML: https://mlsanity.com/paper/1901.01445/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2019.

## Built on by (most cited first, 12 of 12)

- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes](https://mlsanity.com/paper/2503.11979/) (2025)
- [IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments](https://mlsanity.com/paper/2206.13455/) (2022)
- [Forecasting Continuous Non-Conservative Dynamical Systems in SO(3)](https://mlsanity.com/paper/2508.07775/) (2025)
- [Continuous-Time SO(3) Forecasting with Savitzky--Golay Neural Controlled Differential Equations](https://mlsanity.com/paper/2506.06780/) (2025)
- [UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling](https://mlsanity.com/paper/2309.17036/) (2023)
- [Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking](https://mlsanity.com/paper/2201.10602/) (2022)
- [Amirkabir campus dataset: Real-world challenges and scenarios of Visual Inertial Odometry (VIO) for visually impaired people](https://mlsanity.com/paper/2401.03604/) (2024)
- [OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos](https://mlsanity.com/paper/2407.05615/) (2024)
- [Dynamic semantic VSLAM with known and unknown objects](https://mlsanity.com/paper/2412.14359/) (2024)
- [Learning Manifold and Ito Dynamics with Branched Neural Rough Differential Equations](https://mlsanity.com/paper/2606.05272/) (2026)
- [A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs](https://mlsanity.com/paper/2610.08800/) (2026)

## Abstract (arXiv)

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.