Constructing 3D Virtual Reality Objects from 2D Images of Real Objects Using NURBS
Bibliographic record
Abstract
A new method for capturing and reconstructing 3D representations of real objects in a virtual reality system is introduced. Virtual reality applications allow users to navigate and interact with the 3D objects through the environment. This interaction requires that the 3D representation of real objects be highly accurate in modeling the reality. The novelty of the new methodology proposed, consists on the fact that it uses only a high resolution (7 megapixels or higher) digital camera and a projector in conjunction with 3D surface reconstruction techniques based on non-uniform rational Bzier spline (NURBS) functions. The 3D object reconstruction is based on finding unique control points on the 2D images of the object and constructing corresponding 2-D NURBS curves which contain the control points through a process of NURNS fitting. The control points are situated on grid lines which are extracted from the object surface on which a color coded grid is projected. The 2-D NURBS curves are projected into a 3-D space to eventually re-create the 3-D surface of interest. The method does not require any a priori knowledge of the absolute positioning or orientation of the camera and the projector as other 3D reconstruction techniques do. The precision of the method depends on the camera resolution and can attain easily sub-millimeters ranges. Examples illustrate the process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".