Content-Aware 3D Reconstruction with Gaze Data
Bibliographic record
Abstract
3D reconstruction has been shown to be a successful method for creating accurate 3D models out of video data with moving objects. Typically, videos are captured by ordinary cameras; however, more egocentric video footage will be taken by wearable cameras. In this work, we present a 3D reconstruction pipeline that implements content awareness for combining a wearable camera (a scene camera of an eye tracker) with gaze information. The aim is to identify the object of interest (OOI) within the video sequence. The OOI is identified within each frame for boosting the results of classical Structure from Motion (SfM) approaches, using the bio-inspired approach from an earlier study. We implemented a prototype based on the concept of content-aware 3D reconstruction using gaze data. Lastly, we gave an extensive overview of possible use case scenarios in a broad range of fields, starting from spare part reconstruction in difficult-to-access areas to assistive technologies, including exoskeletons and prosthetic arms/hands.
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How this classification was reachedexpand
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 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".