Difference of Normals as a Multi-scale Operator in Unorganized Point Clouds

A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outd...

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Hauptverfasser: Ioannou, Y., Taati, B., Harrap, R., Greenspan, M.
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Greenspan, M.
description A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.
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subjects 3D edges
computer vision
filtering
Image edge detection
Image segmentation
Kernel
KITTI
Laser radar
lidar
multi-scale
Noise
Object recognition
point cloud
segmentation
self-driving car
unorganized
unorganized point clouds
Vectors
title Difference of Normals as a Multi-scale Operator in Unorganized Point Clouds
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