Where to Render: Studying Renderability for IBR of Large-Scale Scenes
Bibliographic record
Abstract
Image-based rendering (IBR) technique enables presenting real scenes interactively to viewers and hence is a key component for implementing VR telepresence. The quality of IBR results depends on the set of pre-captured views, the rendering algorithm used, and the camera parameters of the novel view to be synthesized. Numerous methods were proposed for optimizing the set of captured images and enhancing the rendering algorithms. However, from which regions IBR methods can synthesize satisfactory results is not yet well studied. In this work, we introduce the concept of renderability, which predicts the quality of IBR results at any given viewpoint and view direction. Consequently, the renderability values evaluated for the 5D camera parameter space form a field, which effectively guides viewpoint/trajectory selection for IBR, especially for challenging large-scale 3D scenes. To demonstrate this capability, we designed 2 VR applications: a path planner that allows users to navigate through sparsely captured scenes with controllable rendering quality and a view selector that provides an overview for a scene from diverse and high quality perspectives. We believe the renderability concept, the proposed evaluation method, and the suggested applications will motivate and facilitate the use of IBR in various interactive settings.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".