Deep Learning–Based Scan-to-BIM Automation and Object Scope Expansion Using a Low-Cost 3D Scan Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To bridge the gap in as-built Building Information Model (BIM) creation between the architectural, engineering, and construction (AEC) community and the computer vision community, this paper presents an automated Scan-to-BIM framework for modeling both structural and nonstructural building components using a low-cost scanning data. The state-of-the-art instance-level semantic segmentation algorithm, SoftGroup, is adopted to classify individual building components. Detected wall segments are projected onto a two-dimensional (2D) XY grid, and an interest point detection algorithm, SuperPoint, is used to extract wall corner points. Subsequently, a series of refinement steps is proposed to generate the wall boundary. With optimized parameters, an intersection-over-union of 82.56% was achieved when tested on the benchmark Stanford Three-Dimensional (3D) Indoor Scene Data Set. Our results demonstrated the usability of the proposed wall boundary extraction to the incomplete and complex indoor scan data compared to an existing as-built modeling method. Instance-level segments and the refined wall boundary were combined to generate as-built BIM via parametric modeling.
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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 | 0.001 | 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 | 0.000 |
| 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 it