16 lines
1.2 KiB
Markdown
16 lines
1.2 KiB
Markdown
---
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layout: single
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title: "Point Cloud Segmentation"
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categories: research
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excerpt: "Segmetation of point clouds into primitive building blocks."
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header:
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teaser: assets/figures/4_point_cloud_segmentation_teaser.jpg
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---
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The segmentation and fitting of solid primitives to 3D point clouds is a complex task. Existing systems are restricted either in the number of input points or the supported primitive types. This paper proposes a hybrid pipeline that is able to reconstruct spheres, bounded cylinders and rectangular cuboids on large point sets. It uses a combination of deep learning and classical RANSAC for primitive fitting, a DBSCAN-based clustering scheme for increased stability and a specialized Genetic Algorithm for robust cuboid extraction. In a detailed evaluation, its performance metrics are discussed and resulting solid primitive sets are visualized. The paper concludes with a discussion of the approach’s limitations.
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{% cite friedrich2020hybrid %}
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