# 6D Object Pose Estimation with Depth Images: A Seamless Approach for Robotic Interaction and Augmented Reality

arXiv: https://arxiv.org/abs/1709.01459 · HTML: https://mlsanity.com/paper/1709.01459/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation](https://mlsanity.com/paper/2311.11125/) (2023)
- [Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image](https://mlsanity.com/paper/2204.01586/) (2022)
- [RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery](https://mlsanity.com/paper/2309.10255/) (2023)
- [DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation](https://mlsanity.com/paper/2507.05627/) (2025)
- [FastPoseCNN: Real-Time Monocular Category-Level Pose and Size Estimation Framework](https://mlsanity.com/paper/2406.11063/) (2024)
- [Category Level 6D Object Pose Estimation from a Single RGB Image using Diffusion](https://mlsanity.com/paper/2412.11420/) (2024)

## Abstract (arXiv)

To determine the 3D orientation and 3D location of objects in the surroundings of a camera mounted on a robot or mobile device, we developed two powerful algorithms in object detection and temporal tracking that are combined seamlessly for robotic perception and interaction as well as Augmented Reality (AR). A separate evaluation of, respectively, the object detection and the temporal tracker demonstrates the important stride in research as well as the impact on industrial robotic applications and AR. When evaluated on a standard dataset, the detector produced the highest f1-score with a large margin while the tracker generated the best accuracy at a very low latency of approximately 2 ms per frame with one CPU core: both algorithms outperforming the state of the art. When combined, we achieve a powerful framework that is robust to handle multiple instances of the same object under occlusion and clutter while attaining real-time performance. Aiming at stepping beyond the simple scenarios used by current systems, often constrained by having a single object in absence of clutter, averting to touch the object to prevent close-range partial occlusion, selecting brightly colored objects to easily segment them individually or assuming that the object has simple geometric structure, we demonstrate the capacity to handle challenging cases under clutter, partial occlusion and varying lighting conditions with objects of different shapes and sizes.