Voronoi Rooms: Dynamic Visibility Modulation of Overlapping Spaces for Telepresence
Bibliographic record
Abstract
We propose a multi-user Mixed Reality (MR) telepresence system that allows users to interact by seamlessly visualizing remote environments and avatars overlaid onto their local physical space. Building on prior shared-space approaches, our method first aligns overlapping rooms to maximize a shared space –a common area containing matched real and virtual objects where all users can interact. Uniquely, our system extends beyond this shared space by visualizing non-shared spaces, the remaining part of each room, allowing users to inhabit these distinct areas. To address the issue of overlap between non-shared spaces, we dynamically adjust their visibility based on user proximity, using a Voronoi diagram to prioritize subspaces closer to each user. Visualizing the surrounding space of each user conveys spatial context, helping others interpret their behavior within their environment. Visibility is updated in real time as users move, maintaining a coherent sense of spatial awareness. Through a user study, we demonstrate that our system enhances enjoyment, spatial understanding, and presence compared to shared-space-only approaches. Quantitative results further show that our dynamic visibility modulation improves both personal space preservation and space accessibility relative to static methods. Overall, our system provides users with a seamless, dynamically connected, and shared multi-room environment. We provide an system overview and demo video of our work in the supplementary material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".