Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D Object Recognition By Corresponding and Quantizing Neural 3D Scene
  Representations   We propose a system that learns to detect objects and infer their 3D poses in
RGB-D images. Many existing systems can identify objects and infer 3D poses,
but they heavily rely on human labels and 3D annotations. The challenge here is
to achieve this without relying on strong supervision signals. To address this
challenge, we propose a model that maps RGB-D images to a set of 3D visual
feature maps in a differentiable fully-convolutional manner, supervised by
predicting views. The 3D feature maps correspond to a featurization of the 3D
world scene depicted in the images. The object 3D feature representations are
invariant to camera viewpoint changes or zooms, which means feature matching
can identify similar objects under different camera viewpoints. We can compare
the 3D feature maps of two objects by searching alignment across scales and 3D
rotations, and, as a result of the operation, we can estimate pose and scale
changes without the need for 3D pose annotations. We cluster object feature
maps into a set of 3D prototypes that represent familiar objects in canonical
scales and orientations. We then parse images by inferring the prototype
identity and 3D pose for each detected object. We compare our method to
numerous baselines that do not learn 3D feature visual representations or do
not attempt to correspond features across scenes, and outperform them by a
large margin in the tasks of object retrieval and object pose estimation.
Thanks to the 3D nature of the object-centric feature maps, the visual
similarity cues are invariant to 3D pose changes or small scale changes, which
gives our method an advantage over 2D and 1D methods.

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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org