Augmented Reality Interior Designer Application
Bibliographic record
Abstract
Augmented Reality (AR) is rapidly evolving and reshaping how people engage with digital content, offering experiences that feel both immersive and interactive. This paper presents an AR-powered mobile application designed to enhance the way users visualize furniture and plan interior spaces. The system is built as an Android application, developed in Kotlin using Android Studio, while the backend is handled through PHP and MySQL with phpMyAdmin support. Through the app, users can browse a catalog of 3D furniture models and virtually place them within their real environment using their device's camera. They can scale, rotate, and position objects in real time, helping reduce uncertainty in design decisions by providing accurate spatial visualization. Compared with existing AR interior design solutions, our application demonstrates lower latency, a cleaner and more intuitive interface, and more efficient backend processing. The system's effectiveness was measured through functional testing, user feedback, and response-time analysis, showing strong user satisfaction and reliable performance. Overall, this work contributes to the growing use of AR in interior design and highlights how combining mobile platforms with AR technologies can transform the decision-making process for both clients and designers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.034 |
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".