Bibliographic record
Abstract
This thesis presents a floor planning and furniture layout system called Procrustes, that helps a user to place rooms and furniture in a virtual house. Scenes are generated by considering the user's partial scene description. The resulting floor plans and furniture arrangements can be used in video games to generate scenes that are not of prime importance to the games. We developed the Procrustes Declarative Scene Modelling (DSM) system to reduce the amount of user input compared to existing DSM systems. The name Procrustes is extracted from a Greek mythological character who placed people in an iron bed and ensured that their bodies fit by either cutting their limbs or physically stretching them. The concept of forcing the object to fit in a position is the common feature between our system and the Greek myth. Given a partial scene description, Procrustes extracts hierarchical and spatial relations that constrain the scene. The scene generation process involves floor planning and furniture arrangement. To reduce the amount of input from the user, the sizes of the rooms and furniture objects are set to default values. After all the rooms are placed in the house, the empty spaces scattered throughout the house are reduced and the sizes of the rooms are adjusted correspondingly. To complete the floor plan, a hallway is generated to interconnect the rooms. Then, the furniture objects are placed by considering factors such as purpose, accessibility and visibility. After a set of scenes has been generated, ten of these scenes are presented to the user who response to each one as positive or negative or no input. Based on these responses, more scenes are generated with spatial relations similar to the positive scenes but dissimilar to the negative ones. This process is iterated until a fixed number of 20 iterations is reached or the user is satisfied. The utility of Procrustes is illustrated by generating scenes for several partial scene descriptions. The results are evaluated by comparing the scenes to the descriptions, by checking for wasted space, and by checking the hallway placement.
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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 | 0.001 |
| Open science | 0.001 | 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 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".