Towards Optimizing Spatial Perception of Embedded-Object Visualizations in Optical See-Through Mixed Reality

In this poster, we present work-in-progress research towards improving the spatial perception of 3D objects on Optical See-through Mixed Reality displays, addressing, in particular, visualizations comprising virtual objects embedded inside physical real-world objects. This use-case is common in visualization applications in medicine, engineering and geosciences, but is an added challenge in mixed reality (MR), where multiple modalities of data (real and virtual objects) essentially occupy the same 3D space leading to inevitable depth ambiguity. In this poster, we review several strategies for ameliorating this problem, discuss insights from preliminary implementations of such approaches and propose avenues for future work.

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Publication Details

Published
2024-08-22
DOI
https://doi.org/10.1145/3675231.3678874
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
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article

Towards Optimizing Spatial Perception of Embedded-Object Visualizations in Optical See-Through Mixed Reality

Xuyu Li, John Dingliana, Priyansh Jalan
Visual Attention and Saliency Detection
article

Towards Optimizing Spatial Perception of Embedded-Object Visualizations in Optical See-Through Mixed Reality

Xuyu Li, John Dingliana, Priyansh Jalan
article en

Abstract

In this poster, we present work-in-progress research towards improving the spatial perception of 3D objects on Optical See-through Mixed Reality displays, addressing, in particular, visualizations comprising virtual objects embedded inside physical real-world objects. This use-case is common in visualization applications in medicine, engineering and geosciences, but is an added challenge in mixed reality (MR), where multiple modalities of data (real and virtual objects) essentially occupy the same 3D space leading to inevitable depth ambiguity. In this poster, we review several strategies for ameliorating this problem, discuss insights from preliminary implementations of such approaches and propose avenues for future work.

Trinity College Dublin (IE)
Openalex Percentile: Top 87%
Visual Attention and Saliency Detection
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