Parameter-space ReSTIR for Differentiable and Inverse Rendering
Bibliographic record
Abstract
Differentiable rendering is frequently used in gradient descent-based inverse rendering pipelines to solve for scene parameters – such as reflectance or lighting properties – from target image inputs. Efficient computation of accurate, low variance gradients is critical for rapid convergence. While many methods employ variance reduction strategies, they operate independently on each gradient descent iteration, requiring large sample counts and computation. Gradients may however vary slowly between iterations, leading to unexplored potential benefits when reusing sample information to exploit this coherence. We develop an algorithm to reuse Monte Carlo gradient samples between gradient iterations, motivated by reservoir-based temporal importance resampling in forward rendering. Direct application of this method is not feasible, as we are computing many derivative estimates (i.e., one per optimization parameter) instead of a single pixel intensity estimate; moreover, each of these gradient estimates can affect multiple pixels, and gradients can take on negative values. We address these challenges by reformulating differential rendering integrals in parameter space, developing a new resampling estimator that treats negative functions, and combining these ideas into a reuse algorithm for inverse texture optimization. We significantly reduce gradient error compared to baselines, and demonstrate faster inverse rendering convergence in settings involving complex direct lighting and material textures.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".