Journal
ACM TRANSACTIONS ON GRAPHICS
Volume 29, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1778765.1778831
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Funding
- National Natural Science Foundation of China [60902104]
- National High-tech R&D Program of China [2009AA01Z302]
- Young International Scientists, Shenzhen Science and Technology Foundation [GJ200807210013A]
- Microsoft outstanding young faculty
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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 self-similarities. 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.
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