Orthoscopic elemental image synthesis for 3D light field display using lens design software and real-world captured neural radiance field
Bibliographic record
Abstract
The elemental images (EIs) generation of complex real-world scenes can be challenging for conventional integral imaging (InIm) capture techniques since the pseudoscopic effect, characterized by a depth inversion of the reconstructed 3D scene, occurs in this process. To address this problem, we present in this paper a new approach using a custom neural radiance field (NeRF) model to form real and/or virtual 3D image reconstruction from a complex real-world scene while avoiding distortion and depth inversion. One of the advantages of using a NeRF is that the 3D information of a complex scene (including transparency and reflection) is not stored by meshes or voxel grid but by a neural network that can be queried to extract desired data. The Nerfstudio API was used to generate a custom NeRF-related model while avoiding the need for a bulky acquisition system. A general workflow that includes the use of ray-tracing-based lens design software is proposed to facilitate the different processing steps involved in managing NeRF data. Through this workflow, we introduced a new mapping method for extracting desired data from the custom-trained NeRF-related model, enabling the generation of undistorted orthoscopic EIs. An experimental 3D reconstruction was conducted using an InIm-based 3D light field display (LFD) prototype to validate the effectiveness of the proposed method. A qualitative comparison with the actual real-world scene showed that the 3D reconstructed scene is accurately rendered. The proposed work can be used to manage and render undistorted orthoscopic 3D images from custom-trained NeRF-related models for various InIm applications.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".