Estimating 3D surface properties of natural scenes
Bibliographic record
Abstract
The University of Southampton (UK) and York University (Canada) have collaborated to build the Southampton-York Natural Scenes (SYNS) public dataset. Our goal is to provide a resource that can be used to relate properties of the human visual system to the statistics of natural scenes. At each scene a 3D point cloud was captured with a Leica P20 LiDAR system over a nearly spherical field of view. Registered spherical high dynamic range monocular imagery and panoramic stereo pairs were also recorded. To derive statistical models of natural surfaces, and to relate these surface models to photometric information in associated imagery, we must first develop reliable methods for estimating surface properties from point cloud data. A standard method for identifying the surface normal at a selected 3D point is to compute the smallest eigenvector of the spatial covariance matrix of k points lying closest to selected point. The main problem with this approach is determining the optimal neighbourhood size k. Here we evaluate a novel adaptive method based upon leave-one-out cross-validation. Specifically, at each point we sweep over a broad range of potential neighborhood sizes (k = 4…100), each time computing k estimates of the surface normal based on k-1 points and measuring error as the deviation of the remaining validation point from the estimated tangent plane. The optimal k is that which yields the lowest mean k-fold cross-validation error. We demonstrate that this adaptive method works reliably for diverse scenes in urban and rural environments. Typically a very local neighbourhood (mode of k = 8) is selected, but the distribution has a strong positive tail, particularly for urban environments, where planar surfaces can be estimated more reliably with larger neighbourhoods. We discuss how higher-order geometric surface properties of natural scene data can estimated using similar methods. Meeting abstract presented at VSS 2015
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.002 | 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".