Efficient Image-Space Shape Splatting for Monte Carlo 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
A typical Monte Carlo rendering method contributes one light path only to a single pixel at a time. Reusing light paths across multiple pixels, however, can amortize the cost and improve the efficiency. The state of the art of path reuse is to employ shift mapping to reduce the cost of path reuse, while its computation cost is still proportional to the number of pixels processed in shift mapping. We propose a general framework for efficiently reusing light paths to multiple pixels arranged in arbitrary two-dimensional shapes. Our shape is defined as a set of multiple pixels, and the framework allows us to reuse light paths among pixels in a shape faster than simply evaluating all pixels via shift mapping. The key idea is to sparsely evaluate the contribution of shifted paths at random pixels within the shape and interpolate the contribution to the other pixels. We apply a debiasing estimator to ensure unbiasedness. Our method can be integrated with many existing rendering methods and brings consistent improvement over its single-pixel counterpart.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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