<title>Image-based view synthesis for enhanced perception in teleoperation</title>
Bibliographic record
Abstract
The teleoperation of equipment under impoverished sensing and communication delays, cannot be handled efficiently by conventional remote control techniques. Our approach to this problem is based on an augmented reality control mode in which a graphical model of the equipment is overlaid upon real views from the work-site. A basic capability required in order to produce such an augmented reality mode is the ability to synthesize visual information from new viewpoints based upon existing ones, so as to compensate for the sparsity of real data. Our approach to the problem of image-based view synthesis is based upon the implicit construction of a 3D approximation of the scene, composed of planar triangular patches. New views are then generated by texture-mapping the available real image data onto the reprojected triangles. In order to generate a physically valid joint-triangulation which minimizes the distortions in the rendering of the new view, an iterative approach is utilized. This approach begins with an initial triangulation and refines it iteratively through node-linking alterations and a split and merge process, based upon correlation values between corresponding triangular patches. The paper presents results for both synthetic and real scenes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".