Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of
  3D Scenes   We present NeSF, a method for producing 3D semantic fields from posed RGB
images alone. In place of classical 3D representations, our method builds on
recent work in implicit neural scene representations wherein 3D structure is
captured by point-wise functions. We leverage this methodology to recover 3D
density fields upon which we then train a 3D semantic segmentation model
supervised by posed 2D semantic maps. Despite being trained on 2D signals
alone, our method is able to generate 3D-consistent semantic maps from novel
camera poses and can be queried at arbitrary 3D points. Notably, NeSF is
compatible with any method producing a density field, and its accuracy improves
as the quality of the density field improves. Our empirical analysis
demonstrates comparable quality to competitive 2D and 3D semantic segmentation
baselines on complex, realistically rendered synthetic scenes. Our method is
the first to offer truly dense 3D scene segmentations requiring only 2D
supervision for training, and does not require any semantic input for inference
on novel scenes. We encourage the readers to visit the project website.

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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph