An Improved Fuzzy Decision and Geometric Inference-Based Indoor Layout Estimation Model From RGB-D Images
Bibliographic record
Abstract
Indoor layout estimation is a critical aspect of indoor scene understanding, aiming to recover and reconstruct the geometric structure information of indoor spaces by analyzing images or depth data. The indoor layout estimation is a challenging task due to the complexity of the indoor environment, including the unstructured geometric construction and complex illumination conditions. To address these issues, an improved fuzzy decision and geometric inference-based indoor layout estimation model from red, green, blue, and depth (RGB-D) images is proposed in this article. In the proposed model, to address the challenges of fixed plane detection thresholds, missing wall planes, and depth data loss due to transparent or reflective materials, three main improved modules are presented, namely, the fuzzy decision-based threshold adjustment (FDTA) module, the region growing-based wall supplement (RGWS) module, and the geometric inference-based depth completion (GIDC) module. The FDTA module is used to optimize the plane detection results based on the initial plane detected of the wall and floor to improve the accuracy and robustness of layout estimation. Then, the RGWS module supplements missing wall planes in the preliminary detection results, while the GIDC module completes missing depth information due to transparency or reflectivity in the input images. Experimental results show that the proposed method significantly improves the accuracy and robustness of indoor layout estimation, providing a reliable and efficient solution for complex indoor scenes.
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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 | 0.002 | 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".