# Motion Segmentation by Exploiting Complementary Geometric Models

arXiv: https://arxiv.org/abs/1804.02142 · HTML: https://mlsanity.com/paper/1804.02142/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [Segment Any Motion in Videos](https://mlsanity.com/paper/2503.22268/) (2025)
- [The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields](https://mlsanity.com/paper/2203.00115/) (2022)
- [Motion Segmentation from a Moving Monocular Camera](https://mlsanity.com/paper/2309.13772/) (2023)
- [Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion](https://mlsanity.com/paper/2405.01723/) (2024)
- [WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments](https://mlsanity.com/paper/2601.10716/) (2026)
- [A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation](https://mlsanity.com/paper/2403.01606/) (2024)
- [MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection](https://mlsanity.com/paper/2505.10874/) (2025)
- [Reconstructing Small 3D Objects in front of a Textured Background](https://mlsanity.com/paper/2105.11352/) (2021)
- [Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach](https://mlsanity.com/paper/2406.18837/) (2024)

## Abstract (arXiv)

Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation would lead to difficulty. Even when we are confronted with a general scene-motion, the fundamental matrix approach as a model for motion segmentation still suffers from several defects, which we discuss in this paper. The full potential of the fundamental matrix approach could only be realized if we judiciously harness information from the simpler homography model. From these considerations, we propose a multi-view spectral clustering framework that synergistically combines multiple models together. We show that the performance can be substantially improved in this way. We perform extensive testing on existing motion segmentation datasets, achieving state-of-the-art performance on all of them; we also put forth a more realistic and challenging dataset adapted from the KITTI benchmark, containing real-world effects such as strong perspectives and strong forward translations not seen in the traditional datasets.