Efficient spherical high dynamic range imaging for image-based virtual environments
Bibliographic record
Abstract
Most high dynamic range (HDR) imaging techniques generate HDR radiance maps from exposure bracketed low dynamic range (LDR) images captured with a stationary camera. We propose a novel general framework for spherical HDR imaging for image-based virtual environments from a moving camera. The framework is composed of three major stages: calibration and alignment, spherical stereo matching and HDR composition. In the first stage, camera poses are found and spherical images are rotationally aligned. In the second stage, disparity maps are calculated with a spherical stereo vision toolkit. In the third stage, spherical images are warped from neighboring views to a target view based on enhanced disparity maps, and a spherical HDR radiance map is obtained from the warped exposure bracket. Our method is efficient because we generate a spherical HDR image for each of the viewpoints of the LDR images. We demonstrate our framework on indoor and outdoor scenes and compare our results with two recent state-of-the-art HDR imaging methods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".