Voxel Group Feature Extraction

remotesensing-08-00419-ag_jpegThe search for the Holy Grail continues. This paper from a group of researchers in China describes a voxel group approach to automate the extraction of buildings from mobile lidar surveys.

“In this paper, a new technical framework for automatic and efficient building point extraction is proposed, including three main steps: (1) voxel group-based shape recognition; (2) category-oriented merging; and (3) building point identification by horizontal hollow ratio analysis.

This article proposes a concept of “voxel group” based on the voxelization of VLS points: each voxel group is composed of several voxels that belong to one single real-world object. Then the shapes of point clouds in each voxel group are recognized and this shape information is utilized to merge voxel group.

This article puts forward a characteristic nature of vehicle-borne LiDAR building points, called “horizontal hollow ratio”, for efficient extraction. Experiments are analyzed from two aspects: (1) building-based evaluation for overall experimental area; and (2) point-based evaluation for individual building using the completeness and correctness.

The experimental results indicate that the proposed framework is effective for the extraction of LiDAR points belonging to various types of buildings in large-scale complex urban environments.

 

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One Response to Voxel Group Feature Extraction

  1. If you’re interested in this article, you should definitely take a look at the Auto-Classification feature in Trimble RealWorks.
    An example: https://www.youtube.com/watch?v=hSbXOssmWCE

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