Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implicit Geometric Regularization for Learning Shapes   Representing shapes as level sets of neural networks has been recently proved
to be useful for different shape analysis and reconstruction tasks. So far,
such representations were computed using either: (i) pre-computed implicit
shape representations; or (ii) loss functions explicitly defined over the
neural level sets. In this paper we offer a new paradigm for computing high
fidelity implicit neural representations directly from raw data (i.e., point
clouds, with or without normal information). We observe that a rather simple
loss function, encouraging the neural network to vanish on the input point
cloud and to have a unit norm gradient, possesses an implicit geometric
regularization property that favors smooth and natural zero level set surfaces,
avoiding bad zero-loss solutions. We provide a theoretical analysis of this
property for the linear case, and show that, in practice, our method leads to
state of the art implicit neural representations with higher level-of-details
and fidelity compared to previous methods.

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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
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The categories of this paper are: bayes-an