提出一种紧凑统一的平面图几何编码，提升空间规划与生成效果。
Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications
- 基于自适应单元区域分割，从密度图提取几何感知编码
- 在空间规划与生成任务中精度更高、视觉质量更优
- 适合需要高效几何建模的建筑设计与智能布局场景
本文提出一种统一且紧凑的平面图几何感知编码（Unit Region Encoding），适用于室内空间规划、平面图度量学习及生成等任务。该表示通过基于几何感知密度图聚类的边界自适应单元区域划分，将平面图编码为潜在表示。这些编码由训练好的URE-Net从输入的密集密度图及其他语义图中提取。相较于过度分割的栅格图像和房间级图结构，本方法在不同应用中可灵活切分单元区域，同时实现更高精度与更优视觉质量。我们在多个任务上进行了实验，对比了当前最优方法，验证了该表示的优越性，并通过大量消融实验展示了切分策略的影响。
原文摘要 · Abstract (English)
We present the Unit Region Encoding of floorplans, which is a unified and compact geometry-aware encoding representation for various applications, ranging from interior space planning, floorplan metric learning to floorplan generation tasks. The floorplans are represented as the latent encodings on a set of boundary-adaptive unit region partition based on the clustering of the proposed geometry-aware density map. The latent encodings are extracted by a trained network (URE-Net) from the input dense density map and other available semantic maps. Compared to the over-segmented rasterized images and the room-level graph structures, our representation can be flexibly adapted to different applications with the sliced unit regions while achieving higher accuracy performance and better visual quality. We conduct a variety of experiments and compare to the state-of-the-art methods on the aforementioned applications to validate the superiority of our representation, as well as extensive ablation studies to demonstrate the effect of our slicing choices.
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