Adaptive Neural Kernels for Gradient-domain Rendering
Bibliographic record
Abstract
Monte Carlo methods are a cornerstone of physics-based light transport simulations, valued for their ability to produce high-quality photorealistic images. These stochastic methods often suffer from variance, resulting in undesirable noise in the rendered images. Gradient-domain rendering (GDR) techniques mitigate this problem by estimating unbiased image-space gradients via so-called shift-mapping operators. While these mappings are computationally efficient, they can yield high-variance gradients—and thus poor reconstruction quality—when applied to pixels with wildly different integrals. We tackle this challenge by dynamically selecting the optimal set of neighboring pixels for applying shift-mapping under random sequence replay. Key to our approach is a differentiable sorting network that softly ranks the output of a convolutional neural network conditioned on input sample features for weighted reconstruction. This module is carefully rigidified over time to converge to a hard top-k selection, allowing end-to-end optimization with respect to the reconstruction error. Our method is versatile and can be jointly optimized with other adaptive sampling strategies. We demonstrate variance reduction over other traditional adaptive gradient-domain methods across scenes of varying radiometric complexity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".