Porous Flow and Sediment Transport Simulation for Physically-Based Weathering
Bibliographic record
Abstract
In the graphic community, rendering lifelike scenes remains an open challenge. Among the features required to reach photorealism, aging is the crucial detail that breaks the pristine aspect that is rarely observed in real life. Multiple approaches have been proposed, from example-based to physical simulation, to compute the aging of an object. In this context, we present a simulation framework that handles multiple weathering effects causing an object to alter over time. We identify the key phenomena (wetting, drying, erosion, deposition, and dissolution) and propose a base framework to address the aging process. To tackle this challenge, our method adapts a Smooth Particle Hydrodynamics model to represent external fluids (i.e., classical fluid simulation) and internal porous flow. Our framework handles the wetting, drying, and flow in the porous space in a unified approach. We extend pre-existing sediment transport method to allow the sediment transport outside and inside the porous space. With our method, sediments can be eroded from a solid surface, transported across solid objects through their porous space, and deposited to modify other objects’ properties across the scene.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".