# Instant 3D Object Tracking with Applications in Augmented Reality

arXiv: https://arxiv.org/abs/2006.13194 · HTML: https://mlsanity.com/paper/2006.13194/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V](https://mlsanity.com/paper/2312.09738/) (2023)
- [Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence](https://mlsanity.com/paper/2106.08710/) (2021)

## Abstract (arXiv)

Tracking object poses in 3D is a crucial building block for Augmented Reality applications. We propose an instant motion tracking system that tracks an object's pose in space (represented by its 3D bounding box) in real-time on mobile devices. Our system does not require any prior sensory calibration or initialization to function. We employ a deep neural network to detect objects and estimate their initial 3D pose. Then the estimated pose is tracked using a robust planar tracker. Our tracker is capable of performing relative-scale 9-DoF tracking in real-time on mobile devices. By combining use of CPU and GPU efficiently, we achieve 26-FPS+ performance on mobile devices.