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Spatial Affordance-Aware Affine Transformation Between Heterogeneous Spaces for Mixed Reality Remote Collaboration.

Seonji Kim, Dooyoung Kim, Selin Choi

IEEE Transactions on Visualization and Computer Graphics

|May 14, 2026

Summary

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This study introduces a novel spatial affordance-aware affine transformation for seamless object matching in shared Mixed Reality (MR) spaces. The method enables continuous mapping between diverse physical environments, enhancing alignment and locomotion.

Area of Science:

Computer Vision
Human-Computer Interaction
Robotics

Background:

Existing spatial mapping methods struggle with continuous correspondence between dissimilar physical environments in shared Mixed Reality (MR).
A gap exists in precise object alignment and seamless locomotion across heterogeneous spaces.
Current approaches often rely on physical objects and walkable areas, limiting adaptability.

Purpose of the Study:

To propose a spatial affordance-aware affine transformation method for continuous multi-object matching in shared MR spaces.
To enable seamless mapping between heterogeneous physical environments.
To support precise object alignment and locomotion in shared MR.

Main Methods:

Structurally segmenting heterogeneous spaces into interaction zones.

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Photorealistic Learned Landscapes for Augmented Reality

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Published on: June 27, 2025

Constructing affine patches based on object adjacency and facing configuration.

Utilizing spatial affordances for transformation between dissimilar spaces.

Main Results:

The proposed method achieves broader spatial alignment compared to conventional grid-based methods.
Demonstrates richer multi-object matching capabilities.
Evaluated using a dataset of paired dissimilar spaces.

Conclusions:

The developed method serves as an effective mapping framework for shared MR environments.
It ensures semantic continuity and structural coherence across diverse real-world spaces.
Facilitates robust object correspondence and locomotion in heterogeneous MR settings.