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，一种半监督学习方法，用于训练具备跨数据集和场景鲁棒即用能力的广义单目视觉里程计（VO）模型。与通常依赖已知内参在单一数据集内研究的标准单目VO方法不同，XVO通过学习从视觉场景语义中恢复具有真实尺度的相对位姿——即无需任何已知相机参数。我们利用YouTube上海量无约束异构行车记录仪视频进行自训练，优化运动估计模型。我们的核心贡献体现在两个方面：首先，通过实验证明了半监督训练对学习通用直接VO回归网络的有效性；其次，引入多模态监督（包括分割、光流、深度及音频辅助预测任务），以促进VO任务的广义表征。特别地，我们发现音频预测任务能显著增强半监督学习过程，同时缓解高动态及域外视频数据中的噪声伪标签问题。所提出的教师网络在常用KITTI基准上实现了最先进性能，且无需多帧优化或相机参数信息。结合所提出的半监督步骤，XVO在KITTI、nuScenes及Argoverse等多样场景中展示了即用的知识迁移能力，且无需微调。