ImmerVol: An immersive volume visualization system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Volume visualization is a popular technique for analyzing 3D datasets, especially in the medical domain. An immersive visual environment provides easier navigation through the rendered dataset. However, visualization is only one part of the problem. Finding an appropriate Transfer Function (TF) for mapping color and opacity values in Direct Volume Rendering (DVR) is difficult. This paper combines the benefits of the CAVE Automatic Virtual Environment with a novel approach towards TF generation for DVR, where the traditional low-level color and opacity parameter manipulations are eliminated. The TF generation process is hidden behind a Spherical Self Organizing Map (SSOM). The user interacts with the visual form of the SSOM lattice on a mobile device while viewing the corresponding rendering of the volume dataset in real time in the CAVE. The SSOM lattice is obtained through high-dimensional features extracted from the volume dataset. The color and opacity values of the TF are automatically generated based on the user's perception. Hence, the resulting TF can expose complex structures in the dataset within seconds, which the user can analyze easily and efficiently through complete immersion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it