论文标题
住房源：通过离散且连续降解的扩散模型的矢量平面图生成
HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising
论文作者
论文摘要
该论文通过扩散模型提出了一种新的方法，用于矢量规范生成，该模型将房间/门角的2D坐标具有两个推理目标：1）单步噪声作为连续数量，以精确颠倒连续的前进过程； 2）最终的2D坐标是建立几何事件关系的离散数量，例如并行性，正交性和转角共享。我们的任务是图形的平面图生成，这是平面图中的常见工作流程。我们将平面图表示为1D多边形环，每个循环对应于房间或门。我们的扩散模型在核心上采用了变压器体系结构，该结构基于输入图形构成控制注意力面具，并通过离散且连续的降解过程直接生成矢量图形平面图。我们已经评估了RPLAN数据集的方法。所提出的方法可以对所有指标进行重大改进，以明显的利润率，同时能够产生非曼哈顿结构并控制每个房间的确切拐角数量。一个带有补充视频和文档的项目网站在这里https://aminshabani.github.io/housediffusion。
The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a single-step noise as the continuous quantity to precisely invert the continuous forward process; and 2) the final 2D coordinate as the discrete quantity to establish geometric incident relationships such as parallelism, orthogonality, and corner-sharing. Our task is graph-conditioned floorplan generation, a common workflow in floorplan design. We represent a floorplan as 1D polygonal loops, each of which corresponds to a room or a door. Our diffusion model employs a Transformer architecture at the core, which controls the attention masks based on the input graph-constraint and directly generates vector-graphics floorplans via a discrete and continuous denoising process. We have evaluated our approach on RPLAN dataset. The proposed approach makes significant improvements in all the metrics against the state-of-the-art with significant margins, while being capable of generating non-Manhattan structures and controlling the exact number of corners per room. A project website with supplementary video and document is here https://aminshabani.github.io/housediffusion.
