Keyframe-Based Feed-Forward Visual Odometry
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911392256229376 |
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| author | Dai, Weichen Su, Wenhan Kong, Da Ming, Yuhang Kong, Wanzeng |
| author_facet | Dai, Weichen Su, Wenhan Kong, Da Ming, Yuhang Kong, Wanzeng |
| contents | The emergence of visual foundation models has revolutionized visual odometry~(VO) and SLAM, enabling pose estimation and dense reconstruction within a single feed-forward network. However, unlike traditional pipelines that leverage keyframe methods to enhance efficiency and accuracy, current foundation model based methods, such as VGGT-Long, typically process raw image sequences indiscriminately. This leads to computational redundancy and degraded performance caused by low inter-frame parallax, which provides limited contextual stereo information. Integrating traditional geometric heuristics into these methods is non-trivial, as their performance depends on high-dimensional latent representations rather than explicit geometric metrics. To bridge this gap, we propose a novel keyframe-based feed-forward VO. Instead of relying on hand-crafted rules, our approach employs reinforcement learning to derive an adaptive keyframe policy in a data-driven manner, aligning selection with the intrinsic characteristics of the underlying foundation model. We train our agent on TartanAir dataset and conduct extensive evaluations across several real-world datasets. Experimental results demonstrate that the proposed method achieves consistent and substantial improvements over state-of-the-art feed-forward VO methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_16020 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Keyframe-Based Feed-Forward Visual Odometry Dai, Weichen Su, Wenhan Kong, Da Ming, Yuhang Kong, Wanzeng Computer Vision and Pattern Recognition Robotics The emergence of visual foundation models has revolutionized visual odometry~(VO) and SLAM, enabling pose estimation and dense reconstruction within a single feed-forward network. However, unlike traditional pipelines that leverage keyframe methods to enhance efficiency and accuracy, current foundation model based methods, such as VGGT-Long, typically process raw image sequences indiscriminately. This leads to computational redundancy and degraded performance caused by low inter-frame parallax, which provides limited contextual stereo information. Integrating traditional geometric heuristics into these methods is non-trivial, as their performance depends on high-dimensional latent representations rather than explicit geometric metrics. To bridge this gap, we propose a novel keyframe-based feed-forward VO. Instead of relying on hand-crafted rules, our approach employs reinforcement learning to derive an adaptive keyframe policy in a data-driven manner, aligning selection with the intrinsic characteristics of the underlying foundation model. We train our agent on TartanAir dataset and conduct extensive evaluations across several real-world datasets. Experimental results demonstrate that the proposed method achieves consistent and substantial improvements over state-of-the-art feed-forward VO methods. |
| title | Keyframe-Based Feed-Forward Visual Odometry |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2601.16020 |