Real‐time procedural resurfacing using GPU mesh shader
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".