ZeroVO: Visual Odometry with Minimal Assumptions
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912420882022400 |
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| author | Lai, Lei Yin, Zekai Ohn-Bar, Eshed |
| author_facet | Lai, Lei Yin, Zekai Ohn-Bar, Eshed |
| contents | We introduce ZeroVO, a novel visual odometry (VO) algorithm that achieves zero-shot generalization across diverse cameras and environments, overcoming limitations in existing methods that depend on predefined or static camera calibration setups. Our approach incorporates three main innovations. First, we design a calibration-free, geometry-aware network structure capable of handling noise in estimated depth and camera parameters. Second, we introduce a language-based prior that infuses semantic information to enhance robust feature extraction and generalization to previously unseen domains. Third, we develop a flexible, semi-supervised training paradigm that iteratively adapts to new scenes using unlabeled data, further boosting the models' ability to generalize across diverse real-world scenarios. We analyze complex autonomous driving contexts, demonstrating over 30% improvement against prior methods on three standard benchmarks, KITTI, nuScenes, and Argoverse 2, as well as a newly introduced, high-fidelity synthetic dataset derived from Grand Theft Auto (GTA). By not requiring fine-tuning or camera calibration, our work broadens the applicability of VO, providing a versatile solution for real-world deployment at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08005 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ZeroVO: Visual Odometry with Minimal Assumptions Lai, Lei Yin, Zekai Ohn-Bar, Eshed Computer Vision and Pattern Recognition We introduce ZeroVO, a novel visual odometry (VO) algorithm that achieves zero-shot generalization across diverse cameras and environments, overcoming limitations in existing methods that depend on predefined or static camera calibration setups. Our approach incorporates three main innovations. First, we design a calibration-free, geometry-aware network structure capable of handling noise in estimated depth and camera parameters. Second, we introduce a language-based prior that infuses semantic information to enhance robust feature extraction and generalization to previously unseen domains. Third, we develop a flexible, semi-supervised training paradigm that iteratively adapts to new scenes using unlabeled data, further boosting the models' ability to generalize across diverse real-world scenarios. We analyze complex autonomous driving contexts, demonstrating over 30% improvement against prior methods on three standard benchmarks, KITTI, nuScenes, and Argoverse 2, as well as a newly introduced, high-fidelity synthetic dataset derived from Grand Theft Auto (GTA). By not requiring fine-tuning or camera calibration, our work broadens the applicability of VO, providing a versatile solution for real-world deployment at scale. |
| title | ZeroVO: Visual Odometry with Minimal Assumptions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.08005 |