2017
DOI: 10.1111/cgf.13272
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Data‐Driven Sparse Priors of 3D Shapes

Abstract: We present a sparse optimization framework for extracting sparse shape priors from a collection of 3D models. Shape priors are defined as point-set neighborhoods sampled from shape surfaces which convey important information encompassing normals and local shape characterization. A 3D shape model can be considered to be formed with a set of 3D local shape priors, while most of them are likely to have similar geometry. Our key observation is that the local priors extracted from a family of 3D shapes lie in a ver… Show more

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Cited by 10 publications
(1 citation statement)
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“…Moreover, the works [24,12,13,14] use graph structures and graph Laplacian to capture the local information in the selected neighborhoods and leverage the spatial information [14]. Remil et al [18] utilize the shape priors which are defined as pointset neighborhoods sampled from shape surfaces. However, there are many issues that make it challenging to mine the neighborhood information: First, topology information is not easy to capture with LiDAR scans, which makes it more challenging to estimate vertex normals.…”
Section: Related Workmentioning
confidence: 99%
“…Moreover, the works [24,12,13,14] use graph structures and graph Laplacian to capture the local information in the selected neighborhoods and leverage the spatial information [14]. Remil et al [18] utilize the shape priors which are defined as pointset neighborhoods sampled from shape surfaces. However, there are many issues that make it challenging to mine the neighborhood information: First, topology information is not easy to capture with LiDAR scans, which makes it more challenging to estimate vertex normals.…”
Section: Related Workmentioning
confidence: 99%