This article describes a complete unsupervised system for the segmentation of massive 3D point clouds. Our system bridges the missing components that permit to go from 99% automation to 100% automation for the construction industry. It scales up to billions of 3D points and targets a generic low-level grouping of planar regions usable by a wide range of applications. Furthermore, we introduce a hierarchical multi-level segment definition to cope with potential variations in high-level object definitions.

Datasets:

File structure: $X\; |\; Y\; |\; Z\; |\; R\; |\; G\; |\; B\; |\; Ground\; Truth\; |\; Instance\; Number\; per\; Class$ $$
• PCID5 Area 1
• PCID5 Area 2
• PCID5 Area 3
• PCID5 Area 4
• PCID5 Area 5
• PCID5 Area 6
• PCID8 NAAVIS 1
• PCID8 NAAVIS 2
• PCID10 RWTH CHAIR

File structure: $X\; |\; Y\; |\; Z\; |\; Ground\; Truth\; |\; Instance\; Number\; per\; Class$ $$
• PCID6 REVO INDOOR
• PCID6 REVO OUTDOOR