Adaptive Neural Kernels for Gradient-domain Rendering
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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