Visualizing 3D scenes using non-linear projections and data mining of previous camera movements
Bibliographic record
Abstract
We describe techniques for exploring 3D scenes by combining non-linear projections with the interactive data mining of camera navigations from previous explorations. Our approach is motivated by two key observations: First, that there is a wealth of information in prior explorations of a scene that can assist in future presentations of the same scene. Second, current linear perspective camera models produce images that are too limited to adequately capture the complexity of many 3D scenes. The contributions of this paper are two-fold. First, we show how spatial and temporal subdivision schemes can be used to store camera navigation information that is data mined and clustered to be interactively applicable to a number of existing techniques. Second, we show how the movement of a traditional linear perspective camera is closely tied to non-linear projections that combine space and time. As a result, we present a coherent system where the navigation of a conventional camera is data mined to provide both the understandability of linear perspective and the flexibility of non-linear projection of a 3D scene in real-time. Our system's generality is illustrated by three visualization techniques built with a single data mining and projection infrastructure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".