Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Not Only Look But Observe: Variational Observation Model of Scene-Level
  3D Multi-Object Understanding for Probabilistic SLAM   We present NOLBO, a variational observation model estimation for 3D
multi-object from 2D single shot. Previous probabilistic instance-level
understandings mainly consider the single-object image, not single shot with
multi-object; relations between objects and the entire scene are out of their
focus. The objectness of each observation also hardly join their model.
Therefore, we propose a method to approximate the Bayesian observation model of
scene-level 3D multi-object understanding. By exploiting variational
auto-encoder (VAE), we estimate latent variables from the entire scene, which
follow tractable distributions and concurrently imply 3D full shape and pose.
To perform object-oriented data association and probabilistic simultaneous
localization and mapping (SLAM), our observation models can easily be adopted
to probabilistic inference by replacing object-oriented features with latent
variables.

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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin