Real-Time Panoramic Depth Maps from Omni-directional Stereo Images for 6 DoF Videos in Virtual Reality
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Bibliographic record
Abstract
In this paper we present an approach for 6 DoF panoramic videos from omni-directional stereo (ODS) images using convolutional neural networks (CNNs). More specifically, we use CNNs to generate panoramic depth maps from ODS images in real-time. These depth maps would then allow for re-projection of panoramic images thus providing 6 DoF to a viewer in virtual reality (VR). As the boundaries of a panoramic image must touch in order to envelope a viewer, we introduce a border weighted loss function as well as new error metrics specifically tailored for panoramic images. We show experimentally that training with our border weighted loss function improves performance by benchmarking a baseline skip-connected encoder-decoder style network as well as other state-of-the-art methods in depth map estimation from mono and stereo images. Finally, a practical application for VR using real world data is also demonstrated.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it