ZeroVO: Visual Odometry with Minimal Assumptions

• URL: http://arxiv.org/abs/2506.08005v1
• Date: Mon, 09 Jun 2025 17:59:51 GMT
• Title: ZeroVO: Visual Odometry with Minimal Assumptions
• Authors: Lei Lai, Zekai Yin, Eshed Ohn-Bar,
• Abstract summary: We introduce ZeroVO, a novel visual odometry (VO) algorithm that achieves zero-shot generalization across diverse cameras and environments.<n>We design a calibration-free, geometry-aware network structure capable of handling noise in estimated depth and camera parameters.<n>We analyze complex autonomous driving contexts, demonstrating over 30% improvement against prior methods.
• Score: 5.694070924765915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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