This repository accompanies the paper 'Invisible Textures: Comparing Machine and Human Perception of Environment Texture for AR', in Proceedings of ACM ImmerCom 2023 (co-located with ACM MobiCom 2023), Winner of the ACM ImmerCom 2023 Best Paper Award. In this paper we compare the effect of environment texture on machine perception for augmented reality (AR), i.e., visual-inertial simultaneous localization and mapping (VI-SLAM), with human perception of texture, to inform the design of environments which support good device tracking performance yet have minimal impact on user perception of virtual content.
We perform extensive experiments on the effect of visual texture on VI-SLAM performance using our previously published game engine-based emulator, Virtual-Inertial SLAM. For more information on this tool, implementation code and instructions, and examples of the types of projects it can support, please visit the Virtual-Inertial SLAM GitHub repository.
The motivation for invisible textures arises from the conflicting requirements of machine and human peception in AR. While complex environment textures are beneficial for VI-SLAM-based device pose tracking (and therefore virtual content stability), human perception of virtual content can be negatively impacted by textured environments. This includes both the potential for complex textures to be distracting, as well as background textures being visible through transparent regions of virtual content on AR headsets with optical see-through displays (e.g., the Microsoft HoloLens and Magic Leap headsets).
An example of virtual content transparency on an optical see-through display (on the Magic Leap One AR headset) is shown in the below image. The tree background texture is visible through the virtual assembly instructions, especially in regions of low virtual content luminance:
For our VI-SLAM visual texture experiments we chose the texture images used in 'Predicting Complexity Perception of Real World Images'. This paper also provides the human subjective scores of complexity we use for comparison with human perception of texture.
The set of 112 textures comprises two datasets: 54 images from the VisTex dataset, and 58 images from the RawFooT dataset:
| VisTex | RawFooT |
|---|---|
 |
 |
To study the effect of texture in controlled conditions, we created two different types of virtual environment in Unity. The Room environment was a 6mx6mx6m empty room designed to test the effect of textures in isolation; for each new sequence a texture was applied to the walls, floor and ceiling, as shown below:
The Table environment was designed to replicate the more realistic scenario of an AR-assisted assembly or repair task: an 8mx8mx4m factory environment including a robotic arm and a table with a motorbike engine on it. To create each new sequence in the Table environment the texture was applied to the 1.5mx1.5m table top, as shown below:
To cite Invisible Textures in an academic work, please use:
@inproceedings{Scargill23InvisibleTextures,
title={Invisible textures: Comparing machine and human perception of environment texture for AR},
author={Scargill, Tim and Hadziahmetovic, Majda and Gorlatova, Maria},
booktitle={Proc. ACM Immercom'23 (co-located with ACM MobiCom'23)},
year={2023}
}
The authors of this repository are Tim Scargill and Maria Gorlatova. Contact information of the authors:
- Tim Scargill (timothyjames.scargill AT duke.edu)
- Maria Gorlatova (maria.gorlatova AT duke.edu)
This work was supported in part by NSF grants CSR-1903136, CNS-1908051, and CNS-2112562, NSF CAREER Award IIS-2046072, a Meta Research Award and a CISCO Research Award.