Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas. (June 2015)

• Record Type: Journal Article
• Authors: Weinmann, M.; Urban, S.; Hinz, S.; Jutzi, B.; Mallet, C.
• Is Part Of: Computers & graphics. Volume 49(2015)
• Page Start: 47
• Page End: 57
• Publication Date: 2015-06
• Subjects: 3D scene analysis -- Point cloud -- Feature -- Classification -- Large-scale -- Urban; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2015.01.006
• Languages: English
• ISSNs: 0097-8493

Abstract

Abstract: We propose a new methodology for large-scale urban 3D scene analysis in terms of automatically assigning 3D points the respective semantic labels. The methodology focuses on simplicity and reproducibility of the involved components as well as performance in terms of accuracy and computational efficiency. Exploiting a variety of low-level 2D and 3D geometric features, we further improve their distinctiveness by involving individual neighborhoods of optimal size. Due to the use of individual neighborhoods, the methodology is not tailored to a specific dataset, but in principle designed to process point clouds with a few millions of 3D points. Consequently, an extension has to be introduced for analyzing huge 3D point clouds with possibly billions of points for a whole city. For this purpose, we propose an extension which is based on an appropriate partitioning of the scene and thus allows a successive processing in a reasonable time without affecting the quality of the classification results. We demonstrate the performance of our methodology on two labeled benchmark datasets with respect to robustness, efficiency, and scalability. Abstract : Graphical abstract: We propose a new methodology for large-scale urban 3D scene analysis which is based on distinctive 2D and 3D features derived from optimal neighborhoods. Abstract : Highlights: We present a new methodology for large-scale urban 3D point cloud classification. We analyze a strategy for recovering individual 3D neighborhoods of optimal size. Our methodology involves efficient feature extraction and classification. Our methodology contains an extension towards data-intensive processing. We evaluate our methodology on two recent, publicly available point cloud datasets.