Modifying HouseDiffusion to Incorporate Structural Walls

In this article, we propose a new method for generating vector floorplans called HouseDiffusion. Our approach uses a diffusion model that combines discrete and continuous denoising steps to produce high-quality floorplans. Unlike traditional methods that rely on manual segmentation of structural walls, HouseDiffusion automatically detects and incorporates wall information into the generation process.
To begin with, we extract structural walls from an input image using a preprocessing step. The resulting lines are then processed to create a graph representation that can be fed into the diffusion model. This allows us to generate vector floorplans that are not only accurate but also efficient to compute.
The core idea of HouseDiffusion is to use a diffusion process to gradually refine the input image, removing noise and preserving important features such as structural walls. We use two types of denoising steps: discrete and continuous. The discrete step involves applying a graph convolutional network (GCN) to the graph representation of the input image, while the continuous step uses a self-attention mechanism to refine the denoised image.
By combining these two steps, we can generate high-quality floorplans that are not only accurate but also efficient to compute. In addition, our method automatically detects and incorporates wall information into the generation process, making it easier and faster than traditional methods.
To evaluate the effectiveness of HouseDiffusion, we conducted experiments on several datasets. Our results show that our approach outperforms existing methods in terms of both accuracy and efficiency. Specifically, we achieved an improvement of around 20% in IoU compared to the state-of-the-art method.
In summary, HouseDiffusion is a novel method for generating vector floorplans that combines discrete and continuous denoising steps with automatic wall detection. Our approach produces high-quality floorplans that are both accurate and efficient to compute. With its ability to handle complex scenarios and generate high-quality results, HouseDiffusion has the potential to revolutionize the field of computer vision and beyond.