论文标题
连接点：使用两级查询的平面图重建
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries
论文作者
论文摘要
我们解决了3D扫描的2D平面图重建。现有方法通常采用启发式设计的多阶段管道。取而代之的是，我们将平面平面重建作为单阶段结构化预测任务：找到一组变量大小的多边形集，这又是有序顶点的可变长度序列。为了解决它，我们开发了一种新颖的变压器体系结构，该结构以整体方式平行生成多个房间的多边形，而无需手工制作的中间阶段。该型号具有有关多边形和角落的两级查询，并包括多边形匹配，以使网络端到端训练。我们的方法为两个具有挑战性的数据集（结构3D和SCENECAD）实现了一个新的最新技术，并且比以前的方法要快得多。此外，它可以很容易地扩展以预测其他信息，即语义室类型和架构元素，例如门和窗户。我们的代码和型号可在以下网址找到：https：//github.com/ywyue/roomformer。
We address 2D floorplan reconstruction from 3D scans. Existing approaches typically employ heuristically designed multi-stage pipelines. Instead, we formulate floorplan reconstruction as a single-stage structured prediction task: find a variable-size set of polygons, which in turn are variable-length sequences of ordered vertices. To solve it we develop a novel Transformer architecture that generates polygons of multiple rooms in parallel, in a holistic manner without hand-crafted intermediate stages. The model features two-level queries for polygons and corners, and includes polygon matching to make the network end-to-end trainable. Our method achieves a new state-of-the-art for two challenging datasets, Structured3D and SceneCAD, along with significantly faster inference than previous methods. Moreover, it can readily be extended to predict additional information, i.e., semantic room types and architectural elements like doors and windows. Our code and models are available at: https://github.com/ywyue/RoomFormer.
