Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SurfEmb: Dense and Continuous Correspondence Distributions for Object
  Pose Estimation with Learnt Surface Embeddings   We present an approach to learn dense, continuous 2D-3D correspondence
distributions over the surface of objects from data with no prior knowledge of
visual ambiguities like symmetry. We also present a new method for 6D pose
estimation of rigid objects using the learnt distributions to sample, score and
refine pose hypotheses. The correspondence distributions are learnt with a
contrastive loss, represented in object-specific latent spaces by an
encoder-decoder query model and a small fully connected key model. Our method
is unsupervised with respect to visual ambiguities, yet we show that the query-
and key models learn to represent accurate multi-modal surface distributions.
Our pose estimation method improves the state-of-the-art significantly on the
comprehensive BOP Challenge, trained purely on synthetic data, even compared
with methods trained on real data. The project site is at
https://surfemb.github.io/ .

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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ