# Deep 6-DOF Tracking

arXiv: https://arxiv.org/abs/1703.09771 · HTML: https://mlsanity.com/paper/1703.09771/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 13 of 13)

- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World](https://mlsanity.com/paper/2110.12715/) (2021)
- [Instance Tracking in 3D Scenes from Egocentric Videos](https://mlsanity.com/paper/2312.04117/) (2023)
- [PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking](https://mlsanity.com/paper/2209.07589/) (2022)
- [PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments](https://mlsanity.com/paper/2312.15130/) (2023)
- [GoTrack: Generic 6DoF Object Pose Refinement and Tracking](https://mlsanity.com/paper/2506.07155/) (2025)
- [BCOT: A Markerless High-Precision 3D Object Tracking Benchmark](https://mlsanity.com/paper/2203.13437/) (2022)
- [Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains](https://mlsanity.com/paper/2105.14391/) (2021)
- [Physics-Based Rigid Body Object Tracking and Friction Filtering From RGB-D Videos](https://mlsanity.com/paper/2309.15703/) (2023)
- [Event6D: Event-based Novel Object 6D Pose Tracking](https://mlsanity.com/paper/2603.28045/) (2026)
- [Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping](https://mlsanity.com/paper/2103.05401/) (2021)
- [A Wide-area, Low-latency, and Power-efficient 6-DoF Pose Tracking System for Rigid Objects](https://mlsanity.com/paper/2109.07428/) (2021)

## 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.