R-ProjNet: an optimal rotated-projection neural network for wood segmentation from point clouds
Bibliographic record
Abstract
This work aims to provide a deep learning framework to segment woods from tree point clouds. We develop a novel preprocessing layer before the classical sampling and convolution structure called the projection layer to organize 3D point clouds into 2D points. Input data are transformed into projection data along axis and planes for the subsequent convolution process, which helps decrease the complexity of networks. In order to obtain optimal and effective projection data for capturing local features, we formulate the 2D transformation in the learning process using two learnable angle parameters. The projection map is updated in the learning process for capturing geometric structure information, which plays an important role in wood point segmentation. Experiments show that we have achieved the loss and misclassification error of 0.41% and 8%, respectively, on wood points extraction from handheld laser scanning data. Besides, we also achieve the correctness, completeness and F-score of 90.4%, 91.5% and 0.91, respectively, in a public vehicle laser scanning dataset.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".