# Robust 6DoF Pose Tracking Considering Contour and Interior Correspondence Uncertainty for AR Assembly Guidance

arXiv: https://arxiv.org/abs/2502.11971 · HTML: https://mlsanity.com/paper/2502.11971/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [DeepIM: Deep Iterative Matching for 6D Pose Estimation](https://mlsanity.com/paper/1804.00175/) (2018)
- [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016)
- [Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects](https://mlsanity.com/paper/2203.05334/) (2022)
- [A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking](https://mlsanity.com/paper/1807.02087/) (2018)
- [SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World](https://mlsanity.com/paper/2110.12715/) (2021)
- [Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core](https://mlsanity.com/paper/1911.10249/) (2019)
- [BCOT: A Markerless High-Precision 3D Object Tracking Benchmark](https://mlsanity.com/paper/2203.13437/) (2022)
- [Large-displacement 3D Object Tracking with Hybrid Non-local Optimization](https://mlsanity.com/paper/2207.12620/) (2022)
- [GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance](https://mlsanity.com/paper/2402.07677/) (2024)

## Built on by (most cited first, 1 of 1)

- [Globally Optimal Pose from Orthographic Silhouettes](https://mlsanity.com/paper/2604.09199/) (2026)

## Abstract (arXiv)

Augmented reality assembly guidance is essential for intelligent manufacturing and medical applications, requiring continuous measurement of the 6DoF poses of manipulated objects. Although current tracking methods have made significant advancements in accuracy and efficiency, they still face challenges in robustness when dealing with cluttered backgrounds, rotationally symmetric objects, and noisy sequences. In this paper, we first propose a robust contour-based pose tracking method that addresses error-prone contour correspondences and improves noise tolerance. It utilizes a fan-shaped search strategy to refine correspondences and models local contour shape and noise uncertainty as mixed probability distribution, resulting in a highly robust contour energy function. Secondly, we introduce a CPU-only strategy to better track rotationally symmetric objects and assist the contour-based method in overcoming local minima by exploring sparse interior correspondences. This is achieved by pre-sampling interior points from sparse viewpoint templates offline and using the DIS optical flow algorithm to compute their correspondences during tracking. Finally, we formulate a unified energy function to fuse contour and interior information, which is solvable using a re-weighted least squares algorithm. Experiments on public datasets and real scenarios demonstrate that our method significantly outperforms state-of-the-art monocular tracking methods and can achieve more than 100 FPS using only a CPU.