3D Object Reconstruction From 2D Point Clouds Using Multi-stage Edge Detector
Bibliographic record
Abstract
The research, as described in this paper, is focused on finding a low-cost alternative to modern 3D reconstruction technologies used to recreate objects from orthogonal images. The main objective of the research is to create a 3D shape of an object, applying various software filters (C++ software environment) on six principal views of the object (bottom, top, front, back, left, and right sides), captured as RGB images and utilizing a mathematical algorithm (Python software environment) for final processing. The outputs of the filters are 2D Point Cloud estimations, which are then unionized into the single 3D Point Cloud estimation with the help of calculations in Python code. Generated Point Cloud estimations are then compared to existing 3D CAD models to performa visual inspection to check the performance ofthe algorithm. Additionally, mathematical formulas are used to get the error value in order to check how similarly received estimations are comparing to the actual model. The estimator is tested with total of twenty sample objects.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".