Real-Time Hole-Filling in Mobile Augmented Reality Gaming: A Novel Algorithm to Overcome Depth Sensor Limitations
Bibliographic record
Abstract
In the realm of Augmented Reality (AR) within mobile gaming, the planes recognized by depth sensors delineate the space for content implementation, thereby constraining the scope of representation.Errors in these recognized planes may inhibit the progression of mobile AR.A method is explored that utilizes 'Meshing' facilitated by Unity, generating meshes corresponding to physical space and enabling expansion of content space beyond mere planes.This approach, although promising, is contingent on the depth sensor, leading to the creation of holes beyond the sensor's reach.These holes present a critical issue, allowing game objects to escape.To address this challenge, an algorithm is proposed that consists of two main components: 'Hole-Finding' and 'Hole-Filling'.In 'Hole-Finding', real holes are identified by the calculation of the direction of each loop.Subsequently, 'Hole-Filling' computes the centroid-vertex of each hole and employs it for the hole-filling process.A realtime hole-filling performance with only a 7 μsec degradation was observed, heralding a significant step towards mitigating this problem within AR content.This investigation contributes a novel solution to a crucial technical obstacle, thereby enhancing the functionality and potential of AR in mobile gaming.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".