Estimating local surface attitude from 3D point cloud data.
Bibliographic record
Abstract
The Southampton-York Natural Scenes (SYNS) dataset consists of LiDAR range and image data captured from a variety of natural and built environments. One of our goals is to use the dataset to relate the ecological statistics of 3D surfaces to human perception of surface attitude. A local planar fit at a 3D point in the dataset can be estimated from an eigen-decomposition of a k-neighbourhood of surrounding points. One challenge is to determine the optimal local scale, k; smaller scales produce noisier estimates, while larger scales lead to over-smoothing. We designed and evaluated two algorithms for adaptively selecting the optimal local scale. The first algorithm uses unsupervised leave-one-out cross-validation (XVAL). For each scale k we fit k planes using k-1 points and measured error of fit as the average distance of the left-out point from the plane. The XVAL method assumes white sensor noise. However, in many sensors, including our own Leica P20, internal post-processing produces correlated noise. To address this problem, we evaluated a second, supervised method based on null hypothesis testing (NHT). Under the NHT approach, the surface is assumed to be locally planar unless there is strong evidence to the contrary. In the NHT training phase, we used a planar reference surface to measure the maximum observed mean deviation of points from fitted planes as a function of scale k and distance. In the estimation phase, this function provides an upper bound on the expected deviation for a locally planar surface; we select the maximum k that does not exceed this bound. Both methods tend to select smaller scales for bumpy surfaces and larger scales for flat surfaces. However, by taking noise correlations into account, the NHT method produces more reliable and more accurate surface attitude estimates for a range of different environments. Meeting abstract presented at VSS 2016
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".