Abstract
Bird's-Eye-View (BEV) representation offers a metric-scaled planar workspace, facilitating the simplification of 6-DoF ego-motion to a more robust 3-DoF model for monocular visual odometry (MVO) in intelligent transportation systems. However, existing BEV methods suffer from sparse supervision signals and information loss during perspective-to-BEV projection. We present BEV-ODOM2, an enhanced framework addressing both limitations without additional annotations. Our approach introduces: (1) dense BEV optical flow supervision constructed from 3-DoF pose ground truth for pixel-level guidance; (2) PV-BEV fusion that computes correlation volumes before projection to preserve 6-DoF motion cues while maintaining scale consistency. The framework employs three supervision levels derived solely from pose data: dense BEV flow, 5-DoF for the PV branch, and final 3-DoF output. Enhanced rotation sampling further balances diverse motion patterns in training. Extensive evaluation on KITTI, NCLT, Oxford, and our newly collected ZJH-VO multi-scale dataset demonstrates state-of-the-art performance, achieving 40 improvement in RTE compared to previous BEV methods. The ZJH-VO dataset, covering diverse ground vehicle scenarios from underground parking to outdoor plazas, is publicly available to facilitate future research.
Abstract (translated)
鸟瞰视图（BEV）表示提供了一个度量比例的平面工作空间，有助于将6自由度的自我运动简化为一个更稳健的3自由度模型，从而用于智能交通系统中的单目视觉测距（MVO）。然而，现有的BEV方法由于稀疏的监督信号和透视到BEV投影过程中信息丢失的问题而受到限制。我们提出了BEV-ODOM2，这是一个改进的框架，它在没有额外标注的情况下解决了这些局限性。我们的方法引入了以下两项创新： 1. 从3自由度姿态真实值构建密集的BEV光流监督，为像素级别的指导提供支持。 2. PV-BEV融合计算投影前的相关体积，以保留6自由度运动线索的同时保持比例一致性。 该框架利用仅由姿态数据推导出的三种监督级别：密集的BEV流动、PV分支中的5自由度以及最终的3自由度输出。增强的旋转采样进一步平衡了训练过程中的各种运动模式。在KITTI、NCLT、Oxford和我们新收集的ZJH-VO多尺度数据集上的广泛评估显示，相比之前的BEV方法，在相对位移误差（RTE）上提高了40%的表现。 ZJH-VO数据集涵盖了从地下停车场到户外广场的各种地面车辆场景，并且公开可用以促进未来的研究。
URL
https://arxiv.org/abs/2509.14636
