A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A deep convolutional neural network based on U-Net to predict annual luminance maps
Studying annual luminance maps during the design process provides architects with insight into the space's spatial quality and occupants’ visual comfort. Simulating annual luminance maps is computationally expensive, especially if the objective is to render the scene for multiple viewpoints. This paper proposes a method based on deep learning that accelerates these simulations by predicting the annual luminance maps using only a limited number of rendered high-dynamic-range images. Our proposed model predicts HDR images that are comparable to the rendered ones. Using the transfer learning approach, our model can robustly predict HDR images from other viewpoints in the space with less rendered images and less training required. We evaluated our method using various evaluation metrics, such as MSE, RER, PSNR, SSIM, and runtime duration. Our method shows improvements in all metrics compared to the previous work, especially 33% better MSE loss, 48% more accurate DGP values, and 50% faster runtime.
A deep convolutional neural network based on U-Net to predict annual luminance maps
Studying annual luminance maps during the design process provides architects with insight into the space's spatial quality and occupants’ visual comfort. Simulating annual luminance maps is computationally expensive, especially if the objective is to render the scene for multiple viewpoints. This paper proposes a method based on deep learning that accelerates these simulations by predicting the annual luminance maps using only a limited number of rendered high-dynamic-range images. Our proposed model predicts HDR images that are comparable to the rendered ones. Using the transfer learning approach, our model can robustly predict HDR images from other viewpoints in the space with less rendered images and less training required. We evaluated our method using various evaluation metrics, such as MSE, RER, PSNR, SSIM, and runtime duration. Our method shows improvements in all metrics compared to the previous work, especially 33% better MSE loss, 48% more accurate DGP values, and 50% faster runtime.
A deep convolutional neural network based on U-Net to predict annual luminance maps
Qorbani, Mohammad Ali (author) / Dalirani, Farhad (author) / Rahmati, Mohammad (author) / Hafezi, Mohammad Reza (author)
Journal of Building Performance Simulation ; 15 ; 62-80
2022-01-02
19 pages
Article (Journal)
Electronic Resource
Unknown
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