Aperio: managing 3D scene occlusion using a mechanical tool analogy for visualizing multi-partmesh data.
Bibliographic record
Abstract
This thesis presents a novel interaction model for browsing complex 3D scenes containing numerous layers of occluding and intertwining structures that often hide regions of interest. The interaction model is realized through the development of a custom visualization application, Aperio. Aperio provides a set of virtual mechanical "metal" tools, such as rods, rings, "cookie" cutters and a knife, that support real-time, interactive exploration. Cutter tools are designed to create easily-understood cutaway views (or context-preserving ribbon slices) and rings and rods provide simple path constraints that support rigid transformations of models via "sliding", providing interactive exploded-view capabilities. All tools are based on a single underlying superquadric formulation and can ―"iteratively" be picked up and replanted to generate various views. A multi-pass, GPU-based capping algorithm provides real-time "solid cuts" rendering of surface meshes. We also present a user study to provide supporting evidence of Aperio‘s interaction simplicity and effectiveness for occlusion management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".