Associative Trails

Architectural Drawings Recognition and Generation through Machine Learning (2018)

Notes on a 2018 paper by Weixin Huang and Hao Zheng

A generative adversarial network to superimpose coloured rectangles on architectural floor plans based on room type (Bedroom, kitchen, etc.). Worked really well for some rooms - it even picked up a few mistakes in the human labelling.

Read the paper: ACADIA, ResearchGate, PDF.

  • Pix2PixHD used to generate zoning maps of floor plans
  • Detail improves with number of epochs - ResNet used to prevent overfitting
  • CGAN - Conditional Generative Adversarial Networks - punish trining going in wrong direction
  • Pix2PixHD - 5 groups of convolutional layers, 9 groups of residual network layers, 5 groups of deconvolutional layers
  • Using PyTorch - images from lianjia.com
  • Labels used: Walkway, Bedroom, Living Room, Kitchen, Toilet, Dining Room, Balcony, Window, Door
  • 115 image pairs - 100 training, 15 testing ~ 2hrs to train one network
  • Works well in recognising bedroom, kitchen, toilet & balcony - normally well-defined by walls and specific furniture
  • Less successful for walkway/living room - but some architects got this wrong when labelling test data (done by Architectural students)
  • Struggled with non-orthogonal shapes
  • In one image, GAN found mis-labelled data. Later found 4 errors in human-labelled data, which didn't lead to training in the wrong direction - pretty robust network.
  • Generating plans from zones - sometimes put furniture facing the wrong way. Some architects agreed with the network's furniture position above the position in the test images.
  • 2-pixel convolutions. Residual network avoids overfitting.
  • Trained to 80 epochs - loss value stabilised after that
  • The paper looks at individual neurons' feature detection
  • Bedrooms/balconies often activated together
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