Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: OPD: Single-view 3D Openable Part Detection   We address the task of predicting what parts of an object can open and how
they move when they do so. The input is a single image of an object, and as
output we detect what parts of the object can open, and the motion parameters
describing the articulation of each openable part. To tackle this task, we
create two datasets of 3D objects: OPDSynth based on existing synthetic
objects, and OPDReal based on RGBD reconstructions of real objects. We then
design OPDRCNN, a neural architecture that detects openable parts and predicts
their motion parameters. Our experiments show that this is a challenging task
especially when considering generalization across object categories, and the
limited amount of information in a single image. Our architecture outperforms
baselines and prior work especially for RGB image inputs. Short video summary
at https://www.youtube.com/watch?v=P85iCaD0rfc

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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph