Comparative Evaluation of Deep Neural Network Performance for Point Cloud-Based IFC Object Classification
Bibliographic record
Abstract
Point cloud-based deep neural networks (PC-DNNs) has seen growing interest in the construction domain due to their remarkable ability to enhance Building Information Modeling (BIM)-related tasks. Among these tasks, Industry Foundation Classes (IFC) object classification using PC-DNNs has become an active research topic. This focus aims to mitigate classification discrepancies that occur during the interoperability of BIM tools for information exchange. However, existing studies have not fully investigated the potential of the PC-DNN models for IFC object classification. This limitation is due to the reliance on a limited number of PC-DNN models trained on small, private datasets that are not openly accessible. To address this knowledge gap, this study evaluates diverse state-of-the-art PC-DNN models for IFC object classification. Our study provides a comprehensive analysis of how different PC-DNN components and loss functions affect IFC classification, utilizing two public IFC datasets: IFCNet and BIMGEOM. Experimental results offer a detailed comparison across metrics such as accuracy, learning progression, computation time, and model parameters.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".