6D DBSCAN-based segmentation of building point clouds for planar object classification
6D DBSCAN-based segmentation of building point clouds for planar object classification
复制标题
DOI：
10.1016/j.autcon.2017.12.029
复制
发表时间：
2018-04-01
影响因子：
10.3
通讯作者：
Leite, F.
中科院分区：
文献类型：
--
作者：
Czerniawski, T.;Sankaran, B.;Leite, F.
由于制造和建筑的限制，建筑物和其中的许多人造物体通常是矩形的，由平面部分组成。因此，平面的检测和分析是处理在这些空间中捕获的点云的核心。本文研究了噪声建筑点云中平面物体的语义信息存储问题。考虑的数据集是带有注释的场景网格数据集（SceneNN），这是由消费级深度相机捕获的100多个室内场景的集合。使用一种新的点云分割方法检测数据集中的所有平面物体，该方法在六维聚类空间中应用基于密度的带噪声应用空间聚类（DBSCAN）。在隔离所有平面后，使用特征选择提取和研究描述平面的广泛特征列表。然后使用降维和无监督学习来探索最终特征集和紧急类分组的判别能力。最后，我们训练了一个袋装决策树分类器，该分类器在预测单个平面源自的对象类别方面达到了71.2%的准确率。
Due to constraints in manufacturing and construction, buildings and many of the manmade objects within them are often rectangular and composed of planar parts. Detection and analysis of planes is, therefore, central to processing point clouds captured in these spaces. This paper presents a study of the semantic information stored in the planar objects of noisy building point clouds. The dataset considered is the Scene Meshes Dataset with aNNotations (SceneNN), a collection of over 100 indoor scenes captured by consumer-grade depth cameras. All planar objects within the dataset are detected using a new point cloud segmentation method that applies Density Based Spatial Clustering of Applications with Noise (DBSCAN) in a six dimensional clustering space. With all planes isolated, an extensive list of features describing the planes is extracted and studied using feature selection. Then dimensionality reduction and unsupervised learning are used to explore the discriminative ability of the final feature set as well as emergent class groupings. Finally, we train a bagged decision tree classifier that achieves 71.2% accuracy in predicting the object class from which individual planes originate.