3D Reconstruction of Indoor Corridor Models Using Single Imagery and Video Sequences
Bibliographic record
Abstract
In recent years, 3D indoor modeling has gained more attention due to its role in decision-making process of maintaining the status and managing the security of building indoor spaces. In this thesis, the problem of continuous indoor corridor space modeling has been tackled through two approaches. The first approach develops a modeling method based on middle-level perceptual organization. The second approach develops a visual Simultaneous Localisation and Mapping (SLAM) system with model-based loop closure.\nIn the first approach, the image space was searched for a corridor layout that can be converted into a geometrically accurate 3D model. Manhattan rule assumption was adopted, and indoor corridor layout hypotheses were generated through a random rule-based intersection of image physical line segments and virtual rays of orthogonal vanishing points. Volumetric reasoning, correspondences to physical edges, orientation map and geometric context of an image are all considered for scoring layout hypotheses. This approach provides physically plausible solutions while facing objects or occlusions in a corridor scene.\nIn the second approach, Layout SLAM is introduced. Layout SLAM performs camera localization while maps layout corners and normal point features in 3D space. Here, a new feature matching cost function was proposed considering both local and global context information. In addition, a rotation compensation variable makes Layout SLAM robust against cameras orientation errors accumulations. Moreover, layout model matching of keyframes insures accurate loop closures that prevent miss-association of newly visited landmarks to previously visited scene parts.\nThe comparison of generated single image-based 3D models to ground truth models showed that average ratio differences in widths, heights and lengths were 1.8%, 3.7% and 19.2% respectively. Moreover, Layout SLAM performed with the maximum absolute trajectory error of 2.4m in position and 8.2 degree in orientation for approximately 318m path on RAWSEEDS data set. Loop closing was strongly performed for Layout SLAM and provided 3D indoor corridor layouts with less than 1.05m displacement errors in length and less than 20cm in width and height for approximately 315m path on York University data set. The proposed methods can successfully generate 3D indoor corridor models compared to their major counterpart.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.002 |
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".