3Describe-Creating Tangible AR (Augmented Reality) Objects Using Depth Camera
Bibliographic record
Abstract
The transition from in-person to online classes, accelerated by the global impact of Covid-19, has brought both accessibility and disengagement challenges. While online platforms facilitate learning for distant and international students, the loss of interactive elements diminishes the overall educational experience. This article proposes a novel solution inspired by MIT Professor Dr. Patrick Winston's concept of using “props” to enhance learning. Leveraging augmented reality (AR) technology, an application can be developed to introduce tangible AR elements into the online learning environment. The design and methodology outline the use of Python libraries, including OpenCV and Mediapipe, along with the Intel RealSense D435 depth camera. By employing hand-tracking techniques, real-world coordinates are deduced, allowing the creation of interactive AR objects. Trigonometry is utilized to convert 3D coordinates into 2D projections on the video screen, ensuring accurate representation. The visual perception of depth is achieved by subdividing lines, allowing for the dynamic interaction of virtual objects and real hands. The results and analysis section showcases the functionality of the developed application. A 3D cube or prism appears on-screen, responding to touch and rotation gestures. The collision detection algorithm, assuming a spherical bounding box, determines whether the cube is touched, altering its color and position accordingly. Limitations, such as the imprecise collision area for elongated shapes and potential aliasing issues, are discussed as sources of error. Looking forward, the discussion section explores future enhancements and applications. Incorporating advanced modeling tools like OpenGL or Wavefront could introduce more complex 3D models. Interactive features such as hand gestures for rotation or grabbing could further enrich the online learning experience. This project serves as a foundation for the development of interactive and engaging online learning methods, bridging the gap between physical and virtual educational environments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.039 | 0.009 |
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".