Accurate room layout estimation from multi-view panoramas with multi-label graph cut
Bibliographic record
Abstract
Estimating room layout from panoramas is a new trend in the holistic reconstruction of the 3D environment. However, a single panorama is easily occluded by walls and furniture, making it hard to reconstruct the whole indoor room accurately and completely. Besides, deep learning room layout estimating methods often perform poorly in unseen scenes. To address this need, this paper proposes an accurate room layout estimation method from multi-view panoramas with multi-label graph cut. The proposed method takes full advantage of each panorama by utilizing multi-label graph cut. First, room layouts of each panorama are estimated with pre-trained deep-learning models and projected to the ground as the labels; then, a geometry-aware ray-casting method is utilized to obtain the initial floorplan; next, the initial floorplan is regularized by multi-label graph cut with the estimated labels from each panorama; in the end, the final layouts of each panorama is obtained by transforming the regularized floorplans and estimated ceiling heights into layouts with panorama geometry. Experiments in the recently released multi-view panoramas dataset show that the proposed method can regularize the initial floorplan to a floorplan with accurate geometry. Furthermore, the accuracy of the layouts surpassed the layout estimation accuracy of the single panorama deep learning models (HorizonNet and LGTNet) and the state-of-the-art self-training layout estimation models with multi-view panoramas by a large margin.
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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) | 0.002 | 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 | 0.002 |
| 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".