Artificial Intelligence
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM SIGGRAPH 2003 Papers
An integrating approach to meshing scattered point data
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
Adaptive feature-preserving non-local denoising of static and time-varying range data
Computer-Aided Design
Consolidation of unorganized point clouds for surface reconstruction
ACM SIGGRAPH Asia 2009 papers
LoOP: local outlier probabilities
Proceedings of the 18th ACM conference on Information and knowledge management
Anisotropic smoothing of point sets
Computer Aided Geometric Design - Special issue: Geometric modelling and differential geometry
Technical Section: Robust normal estimation for point clouds with sharp features
Computers and Graphics
Non-local scan consolidation for 3D urban scenes
ACM SIGGRAPH 2010 papers
Technical Section: Orienting unorganized points for surface reconstruction
Computers and Graphics
Adaptive partitioning of urban facades
Proceedings of the 2011 SIGGRAPH Asia Conference
Bilateral Normal Filtering for Mesh Denoising
IEEE Transactions on Visualization and Computer Graphics
Iterative Consolidation of Unorganized Point Clouds
IEEE Computer Graphics and Applications
Fast and Effective Feature-Preserving Mesh Denoising
IEEE Transactions on Visualization and Computer Graphics
2D-3D fusion for layer decomposition of urban facades
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Acquiring 3D indoor environments with variability and repetition
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Post-processing of scanned 3D surface data
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Robust filtering of noisy scattered point data
SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics
Edge-aware point set resampling
ACM Transactions on Graphics (TOG)
International Journal of Computer Vision
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The emergence of laser/LiDAR sensors, reliable multi-view stereo techniques and more recently consumer depth cameras have brought point clouds to the forefront as a data format useful for a number of applications. Unfortunately, the point data from those channels often incur imperfection, frequently contaminated with severe outliers and noise. This paper presents a robust consolidation algorithm for low-quality point data from outdoor scenes, which essentially consists of two steps: 1) outliers filtering and 2) noise smoothing. We first design a connectivity-based scheme to evaluate outlierness and thereby detect sparse outliers. Meanwhile, a clustering method is used to further remove small dense outliers. Both outlier removal methods are insensitive to the choice of the neighborhood size and the levels of outliers. Subsequently, we propose a novel approach to estimate normals for noisy points based on robust partial rankings, which is the basis of noise smoothing. Accordingly, a fast approach is exploited to smooth noise, while preserving sharp features. We evaluate the effectiveness of the proposed method on the point clouds from a variety of outdoor scenes.