BlendMR: A Computational Method To Create Ambient Mixed Reality Interfaces

Violet Han, Hyunsung Cho, Kiyosu Maeda, Alexandra Ion, David Lindlbauer.
Published at ACM ISS 2023
  • Best Paper Award
Teaser image


Mixed Reality (MR) systems display content freely in space, and present nearly arbitrary amounts of information, enabling ubiquitous access to digital information. This approach, however, introduces clutter and distraction if too much virtual content is shown. We present BlendMR, an optimization-based MR system that blends virtual content onto the physical objects in users' environments for ambient information display. Our approach takes existing 2D applications and meshes of physical objects as input. It analyses the geometry of the physical objects and identifies regions that are suitable hosts for virtual elements. Using a novel integer programming formulation, our approach then optimally maps selected contents of the 2D applications onto the object, optimizing for factors such as importance and hierarchy of information, viewing angle, and geometric distortion. We evaluate BlendMR by comparing it to a 2D window baseline. Study results show that BlendMR decreases clutter and distraction, and is preferred by users. We demonstrate the applicability of BlendMR in a series of results and usage scenarios.



@inproceedings {Han2023BlendMR, 
 author = {Han, Violet and Cho, Hyunsung and Maeda, Kiyosu and Ion, Alexandra and Lindlbauer, David}, 
 title = {BlendMR: A Computational Method To Create Ambient Mixed Reality Interfaces}, 
 year = {2023}, 
 publisher = {Association for Computing Machinery}, 
 doi = {10.1145/3626472}, 
 address = {New York, NY, USA}, 
 keywords = {augmented reality, adaptive user interfaces, computational interaction}, 
 location = {Pittsburgh, PA, USA}, 
 series = {ISS '23}