Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Shape Templates with Structured Implicit Functions   Template 3D shapes are useful for many tasks in graphics and vision,
including fitting observation data, analyzing shape collections, and
transferring shape attributes. Because of the variety of geometry and topology
of real-world shapes, previous methods generally use a library of hand-made
templates. In this paper, we investigate learning a general shape template from
data. To allow for widely varying geometry and topology, we choose an implicit
surface representation based on composition of local shape elements. While long
known to computer graphics, this representation has not yet been explored in
the context of machine learning for vision. We show that structured implicit
functions are suitable for learning and allow a network to smoothly and
simultaneously fit multiple classes of shapes. The learned shape template
supports applications such as shape exploration, correspondence, abstraction,
interpolation, and semantic segmentation from an RGB image.

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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex