Non-local scan consolidation for 3D urban scenes

Qian Zheng*, Andrei Sharf, Guowei Wan, Yangyan Li, Niloy J. Mitra, Daniel Cohen-Or, Baoquan Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

Abstract

Recent advances in scanning technologies, in particular devices that extract depth through active sensing, allow fast scanning of urban scenes. Such rapid acquisition incurs imperfections: large regions remain missing, significant variation in sampling density is common, and the data is often corrupted with noise and outliers. However, buildings often exhibit large scale repetitions and selfsimilarities. Detecting, extracting, and utilizing such large scale repetitions provide powerful means to consolidate the imperfect data. Our key observation is that the same geometry, when scanned multiple times over reoccurrences of instances, allow application of a simple yet effective non-local filtering. The multiplicity of the geometry is fused together and projected to a base-geometry defined by clustering corresponding surfaces. Denoising is applied by separating the process into off-plane and in-plane phases. We show that the consolidation of the reoccurrences provides robust denoising and allow reliable completion of missing parts. We present evaluation results of the algorithm on several LiDAR scans of buildings of varying complexity and styles.

Original languageEnglish (US)
Article number94
JournalACM Transactions on Graphics
Volume29
Issue number4
DOIs
StatePublished - Sep 13 2010

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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