Robust Smartphone Screen Integration with Deep Learning for Virtual Reality Pass-through
Bibliographic record
Abstract
Virtual reality is revolutionizing immersive experiences, yet the seamless integration of everyday devices, such as smartphones, remains a challenging frontier. This paper presents a method for dynamically integrating a live smartphone screen into the virtual reality pass-through view. Our approach leverages a lightweight convolutional neural network (CNN) to detect the smartphone in real time, accurately determining its position, scale, and orientation within the camera feed. By synchronizing live screen capture with inertial sensor data, our system computes precise affine transformations that ensure the overlay remains perfectly aligned with its physical counterpart, even during rapid movements. Experimental evaluations demonstrate that our solution achieves an average of 29.2 detection runs per second, delivering a stable and high-fidelity integration without the need for additional hardware. Evaluations also showed a 0.44 increase in detection F1 score during live comparisons to the baseline alternative, which does not use deep learning. This dynamic overlay enhances visual clarity and interaction in virtual reality and bridges the gap between virtual and real-world interfaces, empowering users to access notifications, messages, and productivity applications seamlessly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".