GEOMETRIC CONTEXT AND ORIENTATION MAP COMBINATION FOR INDOOR CORRIDOR MODELING USING A SINGLE IMAGE
Bibliographic record
Abstract
Abstract. Since people spend most of their time indoors, their indoor activities and related issues in health, security and energy consumption have to be understood. Hence, gathering and representing spatial information of indoor spaces in form of 3D models become very important. Considering the available data gathering techniques with respect to the sensors cost and data processing time, single images proved to be one of the reliable sources. Many of the current single image based indoor space modeling methods are defining the scene as a single box primitive. This domain-specific knowledge is usually not applicable in various cases where multiple corridors are joined at one scene. Here, we addressed this issue by hypothesizing-verifying multiple box primitives which represents the indoor corridor layout. Middle-level perceptual organization is the foundation of the proposed method, which relies on finding corridor layout boundaries using both detected line segments and virtual rays created by orthogonal vanishing points. Due to the presence of objects, shadows and occlusions, a comprehensive interpretation of the edge relations is often concealed. This necessitates the utilization of virtual rays to create a physically valid layout hypothesis. Many of the former methods used Orientation Map or Geometric Context to evaluate their proposed layout hypotheses. Orientation map is a map that reveals the local belief of region orientations computed from line segments, and in a segmented image geometric context uses color, texture, edge, and vanishing point cues to estimate the likelihood of each possible label for all super-pixels. Here, the created layout hypotheses are evaluated by an objective function which considers the fusion of orientation map and geometric context with respect to the horizontal viewing angle at each image pixel. Finally, the best indoor corridor layout hypothesis which gets the highest score from the scoring function will be selected and converted to a 3D model. It should be noted that this method is fully automatic and no human intervention is needed to obtain an approximate 3D reconstruction.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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".