# Vision-based Pose Estimation for Augmented Reality : A Comparison Study

arXiv: https://arxiv.org/abs/1806.09316 · HTML: https://mlsanity.com/paper/1806.09316/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2308.05438/) (2023)
- [MFOS: Model-Free & One-Shot Object Pose Estimation](https://mlsanity.com/paper/2310.01897/) (2023)
- [Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation](https://mlsanity.com/paper/2203.07697/) (2022)
- [Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization](https://mlsanity.com/paper/2512.05259/) (2025)
- [Landscape-Awareness for Geometric View Diffusion Model](https://mlsanity.com/paper/2605.19865/) (2026)

## 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.