A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds

Friedrich, Markus; Illium, Steffen; Fayolle, Pierre-Alain; Linnhoff-Popien, Claudia (2020): A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Vol 1: Grapp: pp. 38-48

Abstract

The segmentation and fitting of solid primitives to 3D point clouds is a complex task. Existing systems are restricted either in the number of input points or the supported primitive types. This paper proposes a hybrid pipeline that is able to reconstruct spheres, bounded cylinders and rectangular cuboids on large point sets. It uses a combination of deep learning and classical RANSAC for primitive fitting, a DBSCAN-based clustering scheme for increased stability and a specialized Genetic Algorithm for robust cuboid extraction. In a detailed evaluation, its performance metrics are discussed and resulting solid primitive sets are visualized. The paper concludes with a discussion of the approach's limitations.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Language: English