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Title: Fast Non-Convex Hull Computation
3D surface reconstruction usually begins with a point cloud and aims to build a representation of the object producing that point cloud. There are several algorithms to solve this problem, each with different priors over the point cloud, such as the type of object represented, or the method by which it was obtained. In this work, we focus on an algorithm called Non-Convex Hull (NCH), which reconstructs surfaces through a concept similar to the Medial Axis Transform. A new algorithm called Shrinking Planes is proposed to compute the NCH, based on the Shrinking Ball method with a few improvements. We prove that the new method can approximate surfaces to arbitrarily small error, and evaluate its performance on the surface reconstruction task. The new method maintains the same reconstruction quality as the Naïve Non-Convex Hull method, while achieving a large performance improvement.
Authors:
; ;
Award ID(s):
1717355
Publication Date:
NSF-PAR ID:
10174307
Journal Name:
2019 International Conference on 3D Vision (3DV)
Page Range or eLocation-ID:
747 to 755
Sponsoring Org:
National Science Foundation
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