Monocular Visual Odometry Via Diffusion-Based Joint Learning of Optical Flow and Depth

Abstract

Monocular visual odometry (VO) often suffers from scale ambiguity and interference from moving objects in real-world scenarios. Jointly learning optical flow and depth estimation provides a promising solution for these issues by leveraging their geometric correlation and task complementarity. In this paper, we propose JFD-VO, a novel monocular VO framework that integrates jointly learned optical flow and depth networks. We design a two-stage training process with recursive noise diffusion and a specialized loss function, which enables the model to predict dense and scale-aware depth and optical flow using only readily available sparse LiDAR data and pose ground truth, thereby eliminating the need for expensive and difficult-to-obtain dense annotations. Furthermore, a dedicated masking module is incorporated during joint training to enhance robustness in dynamic environments. Within the VO pipeline, we introduce a Keypoint-weighted Matching Selection module that prioritizes stable features based on forward-backward flow consistency, rather than treating all pixels equally as in conventional optical flow methods. Extensive experiments on public datasets demonstrate the effectiveness of our joint training approach. JFD-VO achieves state-of-the-art accuracy, reducing absolute trajectory error by 14.99% and 27.37% over KPDepth-VO and DF-VO.Code and our self-collected dataset are available at: https://github.com/huqingyuan-9952/JFD-VO.