Spatial Affordance-aware Interactable Subspace Allocation for Mixed Reality Telepresence
Bibliographic record
Abstract
To enable remote Virtual Reality (VR) and Augmented Reality (AR) clients to collaborate as if they were in the same space during Mixed Reality (MR) telepresence, it is essential to overcome spatial heterogeneity and generate a unified shared collaborative environment by integrating remote spaces into a target host space. Especially when multiple remote users connect, a large shared space is necessary for people to maintain their personal space while collaborating, but the existing simple intersection method leads to the creation of narrow shared spaces as the number of remote spaces increases. To robustly align to the host space even as the number of remote spaces increases, we propose a spatial affordance-aware interactable subspace allocation algorithm. The key concept of our approach is to consider the perceivable and interactable areas separately, where every user views the same mutual space, but each remote user has a different interactable subspace, considering their location and spatial affordance. We conducted an evaluation with 900 space combinations, varying the number of remote spaces as two, four, and six, and results show our method outperformed in securing wide interactable mutual space and instantiating users compared to the other spatial matching methods. Our work enables multiple clients from diverse remote locations to access the AR host’s space, allowing them to interact directly with the table, wall, or floor by aligning their physical subspaces within a connected mutual space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".