High-Resolution Hogel Image Generation Using GPU Acceleration
Bibliographic record
Abstract
A holographic stereogram displays reconstructed 3D images by rearranging multiple 2D viewpoint images into small holographic pixels (hogels). However, conventional CPU-based hogel generation processes these images sequentially, causing computation times to soar with as the resolution and number of viewpoints increase, which makes real-time implementation difficult. In this study, we introduce a GPU-accelerated parallel processing method to speed up the generation of high-resolution hogel images and achieve near-real-time performance. Specifically, we implement the pixel-rearrangement algorithm for multiple viewpoint images as a CUDA-based GPU kernel, designing it so that thousands of threads process individual pixels simultaneously. We also optimize CPU–GPU data transfers and improve memory access efficiency to maximize GPU parallel performance. The experimental results show that the proposed method achieves over a 5× speedup compared to the CPU across resolutions from FHD to 8K while maintaining output image quality equivalent to that of the CPU approach. Notably, we confirm near-real-time performance by processing large-scale 8K resolution with 16 viewpoints in just tens of milliseconds. This achievement significantly alleviates the computational bottleneck in large-scale holographic image synthesis, bringing real-time 3D holographic displays one step closer to realization. Furthermore, the proposed GPU acceleration technique is expected to serve as a foundational technology for real-time high-resolution hogel image generation in next-generation immersive display devices such as AR/VR/XR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".