Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud
Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud
复制标题
基于语义线框架的背包式激光扫描点云室内建筑建模
DOI：
10.1016/j.isprsjprs.2018.03.025
复制
发表时间：
2018-09
影响因子：
12.7
通讯作者：
Jonathan Li
中科院分区：
文献类型：
--
作者：
Cheng Wang;Shiwei Hou;Chenglu Wen;Zheng Gong;Qing Li;Xiaotian Sun;Jonathan Li
室内建筑模型在许多室内应用中是必不可少的。这些模型是由建筑物的图元组成的，如天花板、地板、墙壁、窗户和门，而不是室内空间中的可移动物体，如家具。针对室内环境，提出了一种基于语义线框架的新型激光扫描点云数据建模方法。所提出的方法首先将原始点云语义标记为墙壁、天花板、地板和其他对象。然后，从标记点中提取线结构，实现对建筑线框架的初步描述。为了优化由家具遮挡引起的检测到的线结构，构造了条件生成对抗网络（cGAN）深度学习模型。线骨架优化模型包括结构完善、挤出消除和正则化。优化的结果也来自点云的质量评价。因此，数据收集和构建模型表示成为一个统一的任务驱动循环。所提出的方法最终输出一个语义线框架模型，并提供了一个布局的建筑物的内部。实验表明，该方法能有效地提取不同室内场景中的线条骨架。
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.
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DOI：
10.1109/cvpr.2009.5206590
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Daniel Munoz;Andrew Bagnell
通讯作者：
Daniel Munoz;Andrew Bagnell
融合二维激光扫描仪和 RGB-D 相机数据的三维室内移动测绘
影响因子：
4.8
作者：
C. Wen;L. Qin;Q. Zhu;C. Wang;J. Li
通讯作者：
J. Li
使用彩色移动激光雷达点云对道路场景进行基于补丁的语义标记
DOI：
10.1109/tits.2015.2499196
发表时间：
2016
影响因子：
8.5
作者：
Luo Huan;Wang Cheng;Wen Chenglu;Cai Zhipeng;Chen Ziyi;Wang Hanyun;Yu Yongtao;Li Jonathan
通讯作者：
Li Jonathan
影响因子：
19.5
作者：
Desolneux, A;Moisan, L;Morel, JM
通讯作者：
Morel, JM
DOI：
10.5220/0004689601200127
发表时间：
2015-10
期刊：
2014 International Conference on Computer Graphics Theory and Applications (GRAPP)
影响因子：
--
作者：
Sebastian Ochmann;Richard Vock;Raoul Wessel;M. Tamke;R. Klein
通讯作者：
Sebastian Ochmann;Richard Vock;Raoul Wessel;M. Tamke;R. Klein