mlsanityData to 7 Oct 2026

Deep 6-DOF Tracking

arXiv 1703.09771 ↗First posted 28 Mar 2017cs.CVcited by 13

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Abstract (arXiv)

We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded. Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Mar 2017.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 3; 2024: 1; 2025: 1; 2026: 1.

13 papers.

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