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Feature-assisted interactive geometry reconstruction in 3D point clouds using incremental region growing

Authors :
Szabo, Attila
Haaser, Georg
Steinlechner, Harald
Walch, Andreas
Maierhofer, Stefan
Ortner, Thomas
Gröller, Eduard
Source :
Computers & Graphics 111 (2023): 213-224
Publication Year :
2023

Abstract

Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback.<br />Comment: 13 pages, submitted to Computers & Graphics Journal

Details

Database :
arXiv
Journal :
Computers & Graphics 111 (2023): 213-224
Publication Type :
Report
Accession number :
edsarx.2304.05109
Document Type :
Working Paper
Full Text :
https://doi.org/10.1016/j.cag.2023.02.004