GRIHA: synthesizing 2-dimensional building layouts from images captured using a smart phone
Bibliographic record
Abstract
<p>Indoor scene reconstruction and generating a 2D/3D floor plan is a widely explored problem. In the recent years a few algorithms have been proposed, which either use RGB-D images, requiring a depth capturing camera or depend upon panoramic images, assuming little to no occlusion in the room. In this work, we propose a framework named GRIHA (Generating Room Interior of a House using ARcore), which takes advantage of RGB images taken from a conventional mobile phone camera. The proposed work uses Simultaneous Localization And Mapping (SLAM) technology to estimate the 3D transformations required for layout generation. GRIHA uses SLAM based Google ARcore library for camera pose estimation while capturing the images. It gives the user freedom to generate a layout by merely taking a few conventional photos, rather than relying on specialized depth hardware or occlusion-free panoramic images. We have compared GRIHA with other existing methods and obtained superior results. Moreover, the system is tested on multiple hardware platforms to test the dependency and efficiency.</p> <p> </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".