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The official repo for Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality (MobiSys'21)

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# Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality

[Yiqin Zhao](https://yiqinzhao.me), [Tian Guo](https://tianguo.info)

This is the official code release for [Xihe](https://github.com/cake-lab/Xihe/blob/main/xihe_mobisys21.pdf) which was published in MobiSys 2021.

## Overview
**TL;DR:**
No more physical probes or undesirable visual effects!  With our system Xihe, mobile AR developers can access accurate spatially-variant lighting estimation in ~20ms. All you need is a Lidar-enabled device 😄

Omnidirectional lighting provides the foundation for achieving spatially-variant photorealistic 3D rendering, a desirable property for mobile augmented reality applications. However, in practice, estimating omnidirectional lighting can be challenging due to limitations such as partial panoramas of the rendering positions, and the inherent environment lighting and mobile user dynamics. A new opportunity arises recently with the advancements in mobile 3D vision, including built-in high-accuracy depth sensors and deep learning-powered algorithms, which provide the means to better sense and understand the physical surroundings. Centering the key idea of 3D vision, in this work, we design an edge-assisted framework called Xihe to provide mobile AR applications the ability to obtain accurate omnidirectional lighting estimation in real time. Specifically, we develop a novel sampling technique that efficiently compresses the raw point cloud input generated at the mobile device. This technique is derived based on our empirical analysis of a recent 3D indoor dataset and plays a key role in our 3D vision-based lighting estimator pipeline design. To achieve the real-time goal, we develop a tailored GPU pipeline for on-device point cloud processing and use an encoding technique that reduces network transmitted bytes. Finally, we present an adaptive triggering strategy that allows Xihe to skip unnecessary lighting estimations and a practical way to provide temporal coherent rendering integration with the mobile AR ecosystem. We evaluate both the lighting estimation accuracy and time of Xihe using a reference mobile application developed with Xihe's APIs. Our results show that Xihe takes as fast as 20.67ms per lighting estimation and achieves 9.4% better estimation accuracy than a state-of-the-art neural network.

## Paper

[Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality](https://github.com/cake-lab/Xihe/blob/main/xihe_mobisys21.pdf).

If you use the Xihe data or code, please cite:

```bibtex
@InProceedings{xihe_mobisys2021,
    author="Zhao, Yiqin
    and Guo, Tian",
    title="Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality",
    booktitle="The 19th ACM International Conference on Mobile Systems, Applications, and Services",
    year="2021",
}
```

## Directory Structure

- `server`: contains server-side code and relevant information for reproducing the server-side experimental results.
- `reference-app`: contains an Unity3D-based application. This application was developed using Xihe client/server APIs, and can be used for reproducing the remaining experimental results.

We provide detailed instructions for reproducing results in `README.md` files in both the `server` and `reference-app` directories.

## Datasets

One of the key steps in reproducing our work is to generate the transformed point cloud datasets. We have included the detailed dataset generation instructions in [`server/README.md`](./server/README.md#generating-training-dataset). We also provide generated test datasets, and AR session recording data for system testing upon requests. Please email [yzhao11@wpi.edu](mailto:yzhao11@wpi.edu) for requesting the data.

## Acknowledgement

We thank all anonymous reviewers, our shepherd, and our artifact evaluator Tianxing Li for their insight feedback. This work was supported in part by NSF Grants #1755659 and #1815619.

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