Hardware‐Accelerated Rendering of Photo Hulls
Bibliographic record
Abstract
Abstract This paper presents an efficient hardware‐accelerated method for novel view synthesis from a set of images or videos. Our method is based on the photo hull representation, which is the maximal photo‐consistent shape. We avoid the explicit reconstruction of photo hulls by adopting a view‐dependent plane‐sweeping strategy. From the target viewpoint slicing planes are rendered with reference views projected onto them. Graphics hardware is exploited to verify the photo‐consistency of each rasterized fragment. Visibilities with respect to reference views are properly modeled, and only photo‐consistent fragments are kept and colored in the target view. We present experiments with real images and animation sequences. Thanks to the more accurate shape of the photo hull representation, our method generates more realistic rendering results than methods based on visual hulls. Currently, we achieve rendering frame rates of 2–3 fps. Compared to a pure software implementation, the performance of our hardware‐accelerated method is approximately 7 times faster. Categories and Subject Descriptors (according to ACM CCS): CR Categories: I.3.3 [Computer Graphics]: Picture/Image Generation; I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".