Vision-based Pose Estimation for Augmented Reality : A Comparison Study
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Abstract (arXiv)
Augmented reality aims to enrich our real world by inserting 3D virtual objects. In order to accomplish this goal, it is important that virtual elements are rendered and aligned in the real scene in an accurate and visually acceptable way. The solution of this problem can be related to a pose estimation and 3D camera localization. This paper presents a survey on different approaches of 3D pose estimation in augmented reality and gives classification of key-points-based techniques. The study given in this paper may help both developers and researchers in the field of augmented reality.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2018.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2023Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimationcited by 16
- 2023MFOS: Model-Free & One-Shot Object Pose Estimationcited by 10
- 2022Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimationcited by 9
- 2025Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymizationcited by 1
- 2026Landscape-Awareness for Geometric View Diffusion Modelcited by 0
- 2026Landscape-Awareness for Geometric View Diffusion Model19 May 2026
- 2025Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization4 Dec 2025
- 2023MFOS: Model-Free & One-Shot Object Pose Estimation3 Oct 2023
- 2023Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation10 Aug 2023
- 2022Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation15 Mar 2022