Application of Artificial Intelligence Technology and Image-based Interaction Design in Architectural Space Design
Bibliographic record
Abstract
In the field of modern architectural design, the application of artificial intelligence technology and pictorial interaction design is gradually causing revolutionary changes. This paper explores how to integrate these advanced technologies into architectural space design with a view to improving design efficiency and enhancing user experience. Artificial Intelligence Generated Content (AIGC) technology and BIM+VR technology are applied to architectural space design in general, where BIM+VR technology is used for visual modeling of architectural space design solutions and realizing 3D image interaction with users. Specifically, this paper proposes an intelligent assisted design method for architectural space based on Pointnet++ deep learning neural network and a simulation design method for 3D virtual architectural space to realize intelligent and personalized design of architectural space.The average class accuracy and overall assessment accuracy of Pointnet++ trained assessment points reached 83.47% and 76.63% respectively The design scheme given by this model has intelligence, objectivity and authenticity, which can better realize the intelligent assistance for architectural space design. In addition, the 3D virtual architectural space experience system constructed in this paper scores more than 90 points in all experience indicators, with good user experience performance, to meet the user’s image interaction needs, so as to provide a basis for the optimization of architectural space design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".