Indoor building models are essential in many indoor applications. These models are composed of the primitives of the buildings, such as the ceilings, floors, walls, windows, and doors, but not the movable objects in the indoor spaces, such as furniture. This paper presents, for indoor environments, a novel semantic line framework-based modeling building method using backpacked laser scanning point cloud data. The proposed method first semantically labels the raw point clouds into the walls, ceiling, floor, and other objects. Then line structures are extracted from the labeled points to achieve an initial description of the building line framework. To optimize the detected line structures caused by furniture occlusion, a conditional Generative Adversarial Nets (cGAN) deep learning model is constructed. The line framework optimization model includes structure completion, extrusion removal, and regularization. The result of optimization is also derived from a quality evaluation of the point cloud. Thus, the data collection and building model representation become a united task-driven loop. The proposed method eventually outputs a semantic line framework model and provides a layout for the interior of the building. Experiments show that the proposed method effectively extracts the line framework from different indoor scenes.

室内建筑模型在许多室内应用中必不可少。这些模型由建筑物的图元组成，例如天花板，地板，墙壁，窗户和门，但不包括室内空间中的可移动物体（例如家具）。本文针对室内环境，提出了一种新的基于背包式激光扫描点云数据的基于语义线框架的建模方法。所提出的方法首先在语义上将原始点云标记为墙壁，天花板，地板和其他对象。然后从标记的点中提取线结构，以实现对建筑线框架的初始描述。为了优化检测到的由家具遮挡引起的线结构，构建了条件生成对抗网络（cGAN）深度学习模型。线框架优化模型包括结构完成，挤出移除和正则化。优化的结果还来自点云的质量评估。因此，数据收集和构建模型表示成为一个任务驱动的统一循环。所提出的方法最终输出语义线框架模型，并提供建筑物内部的布局。实验表明，该方法有效地从不同的室内场景中提取了线框。所提出的方法最终输出语义线框架模型，并提供建筑物内部的布局。实验表明，该方法有效地从不同的室内场景中提取了线框。所提出的方法最终输出语义线框架模型，并提供建筑物内部的布局。实验表明，该方法有效地从不同的室内场景中提取了线框。