AUTOMATED GENERATION OF HIGH-QUALITY 3D POINT CLOUDS OF ANTLERS USING LOW-COST RANGE CAMERAS
Bibliographic record
Abstract
Abstract. Three-dimensional imaging demonstrates advantages over traditional methods and has already proven feasible for measuring antler growth. However, antlers' velvet-covered surface and irregular structure pose challenges in efficiently obtaining high-quality antler data. Animal data capture using optical imaging devices and point cloud segmentation still require tedious manual work. To obtain 3D data of irregular biological targets like antlers, this paper proposes an automated workflow of high-quality 3D antler point cloud generation using low-cost range cameras. An imaging system of range cameras and one RGB camera is developed for automatic camera triggering and data collection without motion artifacts. The imaging system enables motion detection to ensure data collection occurs without any appreciable animal movement. The antler data are extracted automatically based on a fast k-d tree neighbor search to remove the irrelevant data. Antler point clouds from different cameras captured with various poses are aligned using target-based registration and the normal distribution transformation (NDT). The two-step registration demonstrates precisions of the overall RMSE of 4.8mm for the target-based method and Euclidean fitness score of 10.5mm for the NDT. Complete antler point clouds are generated with a higher density than that of individual frames and improved quality with outliers removed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".