This paper introduces a framework for reconstructing fine-grained room-level models from indoor point clouds. The motivation behind our method stems from the consistent floorwise appearance of building shapes in urban buildings along the vertical direction. To this end, each floor’s points are horizontally sliced to obtain a representative cross-section, from which the linear primitives are detected and enhanced. These linear primitives help to divide the entire space into non-overlapping connected faces with shared edges. These faces are then classified as indoor or outdoor categories by solving a binary energy minimization formulation. The indoor faces are further grouped into each individual rooms with the support of the room semantic map. By propagating and tracing each room’s contour, 2D floor plan can be generated in a semantic-aware manner. These generated 2D floor plans are vertically stretched to match the heights of their respective rooms. Experimental results on six complex scenes from the S3DIS dataset, which encompass both linear and non-linear shapes, demonstrate that our created room models exhibit accurate geometry, correct topology, and rich semantics. The source code of our room-level modeling algorithm is available at https://github.com/indoor-modeling/indoor-modeling.

本文介绍了一种从室内点云重建细粒度房间级模型的框架。我们方法背后的动机源于城市建筑中建筑物形状沿垂直方向的一致的楼层外观。为此，每个楼层的点被水平切片以获得代表性横截面，从中检测和增强线性图元。这些线性图元有助于将整个空间划分为具有共享边缘的不重叠的连接面。然后通过求解二元能量最小化公式将这些面孔分类为室内或室外类别。在房间语义图的支持下，室内面孔被进一步分组为每个单独的房间。通过传播和追踪每个房间的轮廓，可以以语义感知的方式生成 2D 平面图。这些生成的二维平面图被垂直拉伸以匹配各自房间的高度。 S3DIS 数据集中的六个复杂场景（包含线性和非线性形状）的实验结果表明，我们创建的房间模型表现出准确的几何形状、正确的拓扑结构和丰富的语义。我们的房间级建模算法的源代码可在https://github.com/indoor-modeling/indoor-modeling获取。