Real‐time procedural resurfacing using GPU mesh shader
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
Abstract Real‐time rendering of complex environments and detailed objects is challenging due to the geometric generation cost and its associated memory requirements. Traditional methods often rely on precomputed procedural details, limiting flexibility and realtime interaction. Although state‐of‐the‐art approaches have addressed these questions, they frequently fall short in providing dynamic, high‐fidelity surface transformations. This article presents a novel real‐time procedural mesh resurfacing method that utilizes GPU mesh shaders to generate a wide range of geometrical appearances directly in place of a base control mesh. Our approach enables on‐the‐fly procedural geometry generation, allowing for the creation of new explicit geometric surfaces, fine control over geometric adjustments, and dynamic level of detail management. Procedural parameters can be accurately driven in real time by explicit control maps or arbitrary user inputs. The proposed technique reduces VRAM usage and power consumption, offering competitive performance compared to traditional pipelines. Comparative evaluations demonstrate that it enables a significantly higher number of primitives to be rendered in real‐time without being limited by GPU memory. The key advantage of the proposed resurfacing framework lies in its ability to fully control dynamic generation of surfaces at rendertime.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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