We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and settings. In contrast to standard monocular VO approaches which often study a known calibration within a single dataset, XVO efficiently learns to recover relative pose with real-world scale from visual scene semantics, i.e., without relying on any known camera parameters. We optimize the motion estimation model via self-training from large amounts of unconstrained and heterogeneous dash camera videos available on YouTube. Our key contribution is twofold. First, we empirically demonstrate the benefits of semi-supervised training for learning a general-purpose direct VO regression network. Second, we demonstrate multi-modal supervision, including segmentation, flow, depth, and audio auxiliary prediction tasks, to facilitate generalized representations for the VO task. Specifically, we find audio prediction task to significantly enhance the semi-supervised learning process while alleviating noisy pseudo-labels, particularly in highly dynamic and out-of-domain video data. Our proposed teacher network achieves state-of-the-art performance on the commonly used KITTI benchmark despite no multi-frame optimization or knowledge of camera parameters. Combined with the proposed semi-supervised step, XVO demonstrates off-the-shelf knowledge transfer across diverse conditions on KITTI, nuScenes, and Argoverse without fine-tuning.

翻译：我们提出XVO，一种用于训练广义单目视觉里程计（VO）模型的半监督学习方法，能够在多种数据集和场景下实现鲁棒的即插即用操作。与依赖已知相机参数在单一数据集内进行标定的传统单目VO方法不同，XVO通过视觉场景语义高效学习恢复具有真实世界尺度的相对位姿。我们利用YouTube上大量无约束、异构的行车记录仪视频，通过自训练优化运动估计模型。本文的核心贡献有两方面：首先，我们通过实验证明了半监督训练对于学习通用直接VO回归网络的有效性；其次，我们引入多模态监督——包括分割、光流、深度和音频辅助预测任务——以促进VO任务的广义化表征。具体而言，我们发现在高度动态和域外视频数据中，音频预测任务能显著增强半监督学习过程，同时减轻噪声伪标签的影响。我们提出的教师网络在常用KITTI基准上取得了最优性能，且无需多帧优化或相机参数知识。结合所提出的半监督步骤，XVO在KITTI、nuScenes和Argoverse等多种场景下展现了无需微调的即插即用知识迁移能力。