Smartphone Bi-Camera System for Backwards Photometric Correction of a 3D Gaussian Splatted Render
Bibliographic record
Abstract
Abstract. Multi-camera systems are essential for multi-view stereo, enabling accurate depth estimation and 3D reconstruction through parallax. However, traditional bi-camera setups, typically forward-facing, are limited in capturing diverse environmental perspectives, particularly in autonomous driving, where vehicle motion is restricted. To overcome these limitations, we propose a novel no-parallax bi-camera system that combines a forward-facing and a backward-facing camera on a smartphone setup. This configuration simulates 360° spatial coverage and enhances 3D reconstruction by photometrically correcting forward-facing Gaussian-splatted renders using the backward-facing camera in a loosely-coupled manner, rather than relying on multi-view images in a conventional bundle adjustment. Our results show that incorporating a backward-facing image significantly improves the quality of backward renders, effectively reducing artifacts and mitigating overfitting seen in forward-facing-only images, resulting in a more accurate and refined 3D representation of the environment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".