# Vectorization of Floor Plans Based on EdgeGAN
## 4 Experimental Results and Discussion
### EdgeGAN  
In this experiment, all training sets are executed on the hardware platform \"CPU Intel Core i9-9900K, 64 GB memory, and GPU NVIDIA RTX2080 TI\(\times\)2,\" and the software is \"Python 3.6, Pytorch 1.4.0 [36], Cuda 10.0, and Cudnn 7.4.2 [37].\" The maximal training epoch is 220, and the batch size is 128. \(\lambda_{1}\) is always set to 10, and \(\lambda_{2}\) is set to 0 in the first 10 epochs and 100 in the subsequent epochs. The learning rate is set to 0.0002 at the first 20 epochs and decreased to 0 linearly in subsequent epochs. The training is recorded in The quality of generated images can be divided into three levels:  
1. Level 1: The generated images are free from noisy points and have high-quality lines, and the recognition accuracy of primitives is close to the conventional pipeline. The proportion of level 1 is approximately 40%. These images can be used to obtain vector graphics with a few manual adjustments, similar to the conventional pipeline. Figure 10 compares the number of adjusting operations that are counted by a decoration designer on 100 FPs with level 1 results. Although the results of EdgeGAN satisfy the requirements of the application, its performance is still slightly weaker than that of the DL-based pipeline. The mean value of operations of the DL-based pipeline (16.50) is close to that of EdgeGAN (16.67). However, the standard deviation of EdgeGAN (8.34) is much larger than that of the DL-based pipeline (4.4628), which means that the latter is more stable. Moreover, 30 PFMs generated by the DL-based pipeline need less than eight operations, while only 21 PFMs by EdgeGAN, which means that the former has a higher rate of excellence. The results of EdgeGAN. Considering that the pseudo-ground truth annotations themselves are obtained on the basis of the conventional pipeline and suffer from inaccuracy, the results are reasonable. The performance of EdgeGAN can be improved if it is training on a larger and higher quality dataset.
2. Level 2: In addition to inaccurate primitives, some noisy points, broken lines, redundancy lines, or unaligned lines are presented in the generated images, as shown in the lines in the main body of Figure 11. The proportion of level 2 is approximately 55%. The self-supervising loss can relieve but cannot eliminate this phenomenon. Some postprocessing methods are necessary to address these problems. Solving this problem by using the EdgeGAN itself is direct but still challenging.
3. Level 3: Serious defects in quality or accuracy with a proportion of approximately 5% are observed in the sloping walls in Figure 11. The reason is that the number of samples with sloping walls is less than 100, which is much less than horizontal and vertical walls.  
Figure 8: \(G\_filter\_loss\) is 0 in the first 10 epochs and decreases gradually. The \(G\_L1\_loss\) is stable at approximately 1.38 since the 20 th epoch. Thus, it is not a suitable measurement of accuracy. The corresponding evolutionary process of \(Y\) is depicted in Figure 9.  
Figure 9: Generated images in epoch 10, 60, 110, 160, 210.  
On one single RTX2080 TI, the frame rate of EdgeGAN and its postprocessing is approximately 32 fps; and the frame of the DL-based pipeline on an Intel 9900 K CPU is approximately 2 fps. Although EdgeGAN can obtain PFM at a much higher speed, a gap still exists between the integral accuracy and quality of generated images and the requirements of applications.