# A Review on Object Pose Recovery: from 3D Bounding Box Detectors to Full 6D Pose Estimators

arXiv: https://arxiv.org/abs/2001.10609 · HTML: https://mlsanity.com/paper/2001.10609/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 18 of 18)

- [Diffusion Models for Video Prediction and Infilling](https://mlsanity.com/paper/2206.07696/) (2022)
- [SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2106.14193/) (2021)
- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing](https://mlsanity.com/paper/2209.11945/) (2022)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration](https://mlsanity.com/paper/2112.02849/) (2021)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [Depth from Camera Motion and Object Detection](https://mlsanity.com/paper/2103.01468/) (2021)
- [Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators](https://mlsanity.com/paper/2209.07976/) (2022)
- [Bounding Box Disparity: 3D Metrics for Object Detection With Full Degree of Freedom](https://mlsanity.com/paper/2207.03720/) (2022)
- [Open Challenges for Monocular Single-shot 6D Object Pose Estimation](https://mlsanity.com/paper/2302.11827/) (2023)
- [Geometric Change Detection in Digital Twins using 3D Machine Learning](https://mlsanity.com/paper/2103.08201/) (2021)
- [Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding](https://mlsanity.com/paper/2212.01920/) (2022)
- [A Stochastic-Geometrical Framework for Object Pose Estimation based on Mixture Models Avoiding the Correspondence Problem](https://mlsanity.com/paper/2311.18107/) (2023)
- [Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction](https://mlsanity.com/paper/2501.14896/) (2025)
- [Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception](https://mlsanity.com/paper/2506.20045/) (2025)
- [SCOPE: Semantic Conditioning for Sim2Real Category-Level Object Pose Estimation in Robotics](https://mlsanity.com/paper/2509.24572/) (2025)
- [GRC-Pose: Generation-Reconstruction Correspondence for Prior-Free 6D Object Pose Tracking](https://mlsanity.com/paper/2609.39116/) (2026)

## Abstract (arXiv)

Object pose recovery has gained increasing attention in the computer vision field as it has become an important problem in rapidly evolving technological areas related to autonomous driving, robotics, and augmented reality. Existing review-related studies have addressed the problem at visual level in 2D, going through the methods which produce 2D bounding boxes of objects of interest in RGB images. The 2D search space is enlarged either using the geometry information available in the 3D space along with RGB (Mono/Stereo) images, or utilizing depth data from LIDAR sensors and/or RGB-D cameras. 3D bounding box detectors, producing category-level amodal 3D bounding boxes, are evaluated on gravity aligned images, while full 6D object pose estimators are mostly tested at instance-level on the images where the alignment constraint is removed. Recently, 6D object pose estimation is tackled at the level of categories. In this paper, we present the first comprehensive and most recent review of the methods on object pose recovery, from 3D bounding box detectors to full 6D pose estimators. The methods mathematically model the problem as a classification, regression, classification & regression, template matching, and point-pair feature matching task. Based on this, a mathematical-model-based categorization of the methods is established. Datasets used for evaluating the methods are investigated with respect to the challenges, and evaluation metrics are studied. Quantitative results of experiments in the literature are analyzed to show which category of methods best performs across what types of challenges. The analyses are further extended comparing two methods, which are our own implementations, so that the outcomes from the public results are further solidified. Current position of the field is summarized regarding object pose recovery, and possible research directions are identified.