Sparse3DTrack: Monocular 3D Object Tracking Using Sparse Supervision
Monocular 3D object tracking aims to estimate temporally consistent 3D object poses across video frames, enabling autonomous agents to reason about scene dynamics. However, existing state-of-the-art approaches are fully supervised and rely on dense 3D annotations over long video sequences, which are expensive to obtain and difficult to scale. In this work, we address this fundamental limitation by proposing the first sparsely supervised framework for monocular 3D object tracking. Our approach decomposes the task into two sequential sub-problems: 2D query matching and 3D geometry estimation. Both components leverage the spatio-temporal consistency of image sequences to augment a sparse set...
| Authors | Nikhil Gosala, B. Ravi Kiran, Senthil Yogamani et al. |
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| Primary category | cs.RO |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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