Segmentation and Classification of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Matrix computations (3rd ed.)
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Continuous Shading of Curved Surfaces
IEEE Transactions on Computers
On the normal vector estimation for point cloud data from smooth surfaces
Computer-Aided Design
Realtime segmentation of range data using continuous nearest neighbors
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Realtime segmentation of range data using continuous nearest neighbors
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Shadow segmentation using time-of-flight cameras
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Real-time rendering of massive unstructured raw point clouds using screen-space operators
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
SMI 2013: Point cloud normal estimation via low-rank subspace clustering
Computers and Graphics
An adaptive normal estimation method for scanned point clouds with sharp features
Computer-Aided Design
Modeling and correction of multipath interference in time of flight cameras
Image and Vision Computing
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As mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3D object recognition plays an increasingly important role. One of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. Although there exists a plethora of different approaches for estimating normal vectors from 3D point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. This paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. The study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the 'right' algorithm for the robotics practitioner. The robustness of the methods with respect to noise and neighborhood size is analyzed. All algorithms are benchmarked with simulated as well as real 3D laser data obtained from a mobile robot.