# Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

arXiv: https://arxiv.org/abs/2106.08710 · HTML: https://mlsanity.com/paper/2106.08710/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 301 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Learning to Track at 100 FPS with Deep Regression Networks](https://mlsanity.com/paper/1604.01802/) (2016)
- [VITAL: VIsual Tracking via Adversarial Learning](https://mlsanity.com/paper/1804.04273/) (2018)
- [Automatic Scene Inference for 3D Object Compositing](https://mlsanity.com/paper/1912.12297/) (2019)
- [Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking](https://mlsanity.com/paper/1607.05781/) (2016)
- [Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks](https://mlsanity.com/paper/1706.07684/) (2017)
- [Interaction Methods for Smart Glasses](https://mlsanity.com/paper/1707.09728/) (2017)
- [GrabAR: Occlusion-aware Grabbing Virtual Objects in AR](https://mlsanity.com/paper/1912.10637/) (2019)
- [Object Detection in the Context of Mobile Augmented Reality](https://mlsanity.com/paper/2008.06655/) (2020)
- [Instant 3D Object Tracking with Applications in Augmented Reality](https://mlsanity.com/paper/2006.13194/) (2020)
- [Light Source Estimation with Analytical Path-tracing](https://mlsanity.com/paper/1701.04101/) (2017)
- [Semantic-Aware Label Placement for Augmented Reality in Street View](https://mlsanity.com/paper/1912.07105/) (2019)

## Built on by (most cited first, 10 of 10)

- [Improving Diffusion Models for Scene Text Editing with Dual Encoders](https://mlsanity.com/paper/2304.05568/) (2023)
- [RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction](https://mlsanity.com/paper/2512.23437/) (2025)
- [MoEdit: On Learning Quantity Perception for Multi-object Image Editing](https://mlsanity.com/paper/2503.10112/) (2025)
- [FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework](https://mlsanity.com/paper/2308.02905/) (2023)
- [Virtual Occlusions Through Implicit Depth](https://mlsanity.com/paper/2305.07014/) (2023)
- [NaviSense: A Multimodal Assistive Mobile application for Object Retrieval by Persons with Visual Impairment](https://mlsanity.com/paper/2509.18672/) (2025)
- [PILAR: Personalizing Augmented Reality Interactions with LLM-based Human-Centric and Trustworthy Explanations for Daily Use Cases](https://mlsanity.com/paper/2512.17172/) (2025)
- [iTrace: Click-Based Gaze Visualization on the Apple Vision Pro](https://mlsanity.com/paper/2508.12268/) (2025)
- [Augmented Assembly: Object Recognition and Hand Tracking for Adaptive Assembly Instructions in Augmented Reality](https://mlsanity.com/paper/2601.11535/) (2025)
- [Systematic Literature Review of Machine Learning Models and Applications for Text Recognition](https://mlsanity.com/paper/2608.26500/) (2026)

## Abstract (arXiv)

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and performs seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences by using MAR devices to provide universal accessibility to digital contents. Over the past 20 years, a number of MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discusses the latest studies on MAR through a top-down approach: 1) MAR applications; 2) MAR visualisation techniques adaptive to user mobility and contexts; 3) systematic evaluation of MAR frameworks including supported platforms and corresponding features such as tracking, feature extraction plus sensing capabilities; and 4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields, current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.