A Two Pass Fuzzy Logic-based Approach for Efficient Rendering of Point Sampled Surfaces
Bibliographic record
Abstract
Point sampled surface models are gaining tremendous popularity in 3D gaming and cinema due to the recent advances in 3D object scanning technology. Splatting is the standard technique used for rendering mainly to ensure the absence of holes due to inadequate sampling, and the need for visual smoothness in the final rendered image. Since a pixel may be covered by multiple splats, high fidelity rendering requires correct visibility computations and blending at every pixel. In this paper, we present a two pass fuzzy logic based approach for increasing rendering efficiency. Each splat belongs to pixels in a fuzzy way with probability reducing as the distance from centre of splat is increased. The first pass for visibility computation is a fast one as it is computed at lower resolution. In second pass, we blend the splats which contribute to a pixel. For blending, we use a simple fuzzy rule to find the contribution of each splat. We have carried out a complete implementation of this technique and the results clearly demonstrate the performance gain when rendering large point sampled surface models
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".