# Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions

arXiv: https://arxiv.org/abs/1808.00274 · HTML: https://mlsanity.com/paper/1808.00274/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking](https://mlsanity.com/paper/2205.02301/) (2022)
- [AirDOS: Dynamic SLAM benefits from Articulated Objects](https://mlsanity.com/paper/2109.09903/) (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)
- [AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments](https://mlsanity.com/paper/2108.01246/) (2021)
- [UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling](https://mlsanity.com/paper/2309.17036/) (2023)
- [PoseFusion2: Simultaneous Background Reconstruction and Human Shape Recovery in Real-time](https://mlsanity.com/paper/2108.00695/) (2021)
- [Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking](https://mlsanity.com/paper/2201.10602/) (2022)
- [PickScan: Object discovery and reconstruction from handheld interactions](https://mlsanity.com/paper/2411.11196/) (2024)

## Abstract (arXiv)

Estimating motion from images is a well-studied problem in computer vision and robotics. Previous work has developed techniques to estimate the motion of a moving camera in a largely static environment (e.g., visual odometry) and to segment or track motions in a dynamic scene using known camera motions (e.g., multiple object tracking). It is more challenging to estimate the unknown motion of the camera and the dynamic scene simultaneously. Most previous work requires a priori object models (e.g., tracking-by-detection), motion constraints (e.g., planar motion), or fails to estimate the full SE(3) motions of the scene (e.g., scene flow). While these approaches work well in specific application domains, they are not generalizable to unconstrained motions. This paper extends the traditional visual odometry (VO) pipeline to estimate the full SE(3) motion of both a stereo/RGB-D camera and the dynamic scene. This multimotion visual odometry (MVO) pipeline requires no a priori knowledge of the environment or the dynamic objects. Its performance is evaluated on a real-world dynamic dataset with ground truth for all motions from a motion capture system.