MLS2: Sharpness Field Extraction Using CNN for Surface Reconstruction
Bibliographic record
Abstract
We address the challenging problem of reconstructing surfaces with sharp features from unstructured and noisy point clouds. For smooth surfaces, moving least squares (MLS) has been a popular method. MLS variants for dealing with sharp features have been proposed, though they have not been as successful. Our take on this problem is very different. By training a convolutional neural network (CNN), we first derive a sharpness field parametrized over the underlying smooth proxy MLS surface. This field provides us two benefits - (i) it enables us to both detect and reconstruct sharp features, this time using an anisotropic MLS kernel, while preserving most of the MLS reconstruction method's properties, and (ii) unlike classification based methods, it does not require that sharp features be present only at input points. With just a small amount of training data, we demonstrate our results on a set of illustrative test cases and compare qualitatively and quantatively with results from MLS variants and the more recent PointNet deep learning network.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".