# Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects

arXiv: https://arxiv.org/abs/2201.13278 · HTML: https://mlsanity.com/paper/2201.13278/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects](https://mlsanity.com/paper/1809.10790/) (2018)
- [BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth](https://mlsanity.com/paper/1703.10896/) (2017)
- [Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation](https://mlsanity.com/paper/1908.07433/) (2019)
- [DPOD: 6D Pose Object Detector and Refiner](https://mlsanity.com/paper/1902.11020/) (2019)
- [HybridPose: 6D Object Pose Estimation under Hybrid Representations](https://mlsanity.com/paper/2001.01869/) (2020)
- [Self6D: Self-Supervised Monocular 6D Object Pose Estimation](https://mlsanity.com/paper/2004.06468/) (2020)
- [BlenderProc](https://mlsanity.com/paper/1911.01911/) (2019)
- [Photorealistic Image Synthesis for Object Instance Detection](https://mlsanity.com/paper/1902.03334/) (2019)
- [Efficient Semantic Image Synthesis via Class-Adaptive Normalization](https://mlsanity.com/paper/2012.04644/) (2020)
- [A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking](https://mlsanity.com/paper/1807.02087/) (2018)
- [PyraPose: Feature Pyramids for Fast and Accurate Object Pose Estimation under Domain Shift](https://mlsanity.com/paper/2010.16117/) (2020)
- [Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images](https://mlsanity.com/paper/2003.12344/) (2020)

## Built on by (most cited first, 3 of 3)

- [CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation](https://mlsanity.com/paper/2210.05318/) (2022)
- [MaskVal: Simple but Effective Uncertainty Quantification for 6D Pose Estimation](https://mlsanity.com/paper/2409.03556/) (2024)
- [Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training](https://mlsanity.com/paper/2402.04979/) (2024)

## Abstract (arXiv)

In this paper, we present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects. The combination of a convolutional neural network for object classification and rough pose estimation with a local pose refinement and an automatic mismatch detection enables direct application in real-time AR scenarios. A new network architecture, trained solely with synthetic images, allows simultaneous pose estimation of multiple objects with reduced GPU memory consumption and enhanced performance. In addition, the pose estimates are further improved by a local edge-based refinement step that explicitly exploits known object geometry information. For continuous movements, the sole use of local refinement reduces pose mismatches due to geometric ambiguities or occlusions. We showcase the entire tracking pipeline and demonstrate the benefits of the combined approach. Experiments on a challenging set of non-textured similar objects demonstrate the enhanced quality compared to the baseline method. Finally, we illustrate how the system can be used in a real AR assistance application within the field of construction.