EDGE BASED 3D INDOOR CORRIDOR MODELING USING A SINGLE IMAGE
Bibliographic record
Abstract
Abstract. Reconstruction of spatial layout of indoor scenes from a single image is inherently an ambiguous problem. However, indoor scenes are usually comprised of orthogonal planes. The regularity of planar configuration (scene layout) is often recognizable, which provides valuable information for understanding the indoor scenes. Most of the current methods define the scene layout as a single cubic primitive. This domain-specific knowledge is often not valid in many indoors where multiple corridors are linked each other. In this paper, we aim to address this problem by hypothesizing-verifying multiple cubic primitives representing the indoor scene layout. This method utilizes middle-level perceptual organization, and relies on finding the ground-wall and ceiling-wall boundaries using detected line segments and the orthogonal vanishing points. A comprehensive interpretation of these edge relations is often hindered due to shadows and occlusions. To handle this problem, the proposed method introduces virtual rays which aid in the creation of a physically valid cubic structure by using orthogonal vanishing points. The straight line segments are extracted from the single image and the orthogonal vanishing points are estimated by employing the RANSAC approach. Many scene layout hypotheses are created through intersecting random line segments and virtual rays of vanishing points. The created hypotheses are evaluated by a geometric reasoning-based objective function to find the best fitting hypothesis to the image. The best model hypothesis offered with the highest score is then converted to a 3D model. The proposed method is fully automatic and no human intervention is necessary to obtain an approximate 3D reconstruction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".