Official implementation of the AAAI 2025 paper: "GSDiff: Synthesizing Vector Floorplans via Geometry-enhanced Structural Graph Generation"
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Create folder
datasets/rplandata/Data. -
Download the 80,788 RPLAN dataset (http://staff.ustc.edu.cn/~fuxm/projects/DeepLayout/index.html). It contains a
floorplan_datasetfolder. Place thisfloorplan_datasetfolder underdatasets/rplandata/Data. -
Run the following scripts to obtain structural graph data:
python rplan-extract.py python rplan-process1.py python rplan-process2.py python rplan-process3.py python rplan-process4.py
- After completion, a directory
rplang-v3-withsemantics(65,763 train + 3,000 val + 3,000 test = 71,763.npyfiles) will be created underdatasets/rplandata/Data. - Running
rplan-extract.pyalso generates folders (1-channel-semantics-256,3-channel-semantics-256,bin_imgs,e_imgs, etc.). You can remove them.
- After completion, a directory
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Run the following scripts to obtain structural graph data with boundaries:
python rplan-process5.py python rplan-process6.py python rplan-process7.py
- After completion, 2 directories
rplang-v3-withsemantics-withboundaryandrplang-v3-withsemantics-withboundary-v2, will appear underdatasets/rplandata/Data, each containing 71,763 files.
- After completion, 2 directories
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Run the following scripts to obtain topology graphs:
python rplan-process8.py python rplan-process9.py python rplan-process10.py
- After completion, a
rplang-v3-bubble-diagramfolder will be created underdatasets/rplandata/Data, containing the same number of files.
- After completion, a
Note, when we conducted our experiments, the semantics of bubble diagram GT involved randomness. Due to the terms of the RPLAN dataset, we are not permitted to release any part of it. Therefore, the bubble diagram GT semantics extracted using the provided scripts may differ slightly from our experimental results. However, given the large data scale, the bias should be minor (for rooms with ambiguous categories, both our GT and the GT you extract randomly select one category, which is no statistically significant difference).
Alternatively, you can use get_cycle_basis_and_semantic_3_semansimplified instead of get_cycle_basis_and_semantic_2_semansimplified in rplan-process8/9/10.py to extract room semantics and train your own topology models. This method is not random, may yield improvements over the metrics reported in the paper.
The test scripts for no constraints, topology constraints, and boundary constraints are all placed under scripts (test_xxx.py).
Download the corresponding weights and run them via:
python test_xxx.pyNo constraints: We use the original 3000 results and run them 5 times to get the average.
Topology constraints: We took the intersection of the original 3000 results with the test set numbers of HouseDiffusion and House-GAN++, and got 757.
We ran them 5 times and averaged them to get the FID, KID, GED, and statistical analysis of each room type.
The sample numbers of 757 are in line 183 of evalmetric-topoconstrain-ged-roomnumber.py.
Boundary constraints: We took the intersection of the original 3000 results with the test set numbers of HouseDiffusion and House-GAN++, and got 378.
We ran them 5 times and averaged them to get the FID, KID, GED, and statistical analysis of each room type.
The sample number of 378 is on line 9 of evalmetric-boun-constrain-fid-kid.py.
unconstrained params: https://drive.google.com/file/d/15gM0GtW2GwHmlpz0r-rpvo-k-BlNy_gu/view?usp=sharing
topology-constrained params: https://drive.google.com/file/d/1pk7SmvLZ8ON3OUL3SNxPRu73ndVKru0z/view?usp=sharing
boundary-constrained params: https://drive.google.com/file/d/1XMU5zxXoxiXg3LB9rlluWOX9BT2YIGdK/view?usp=sharing
boundary-autoencoder CNN params: https://drive.google.com/file/d/1l6QRpfX5Jtucg3R995HajlwRG8SewUJW/view?usp=sharing
topology-autoencoder Transformer params: https://drive.google.com/file/d/1tExX8LdrFpJfBQH5y2emC6BltBwf9tHx/view?usp=sharing