Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Seeing Glass: Joint Point Cloud and Depth Completion for Transparent
  Objects   The basis of many object manipulation algorithms is RGB-D input. Yet,
commodity RGB-D sensors can only provide distorted depth maps for a wide range
of transparent objects due light refraction and absorption. To tackle the
perception challenges posed by transparent objects, we propose TranspareNet, a
joint point cloud and depth completion method, with the ability to complete the
depth of transparent objects in cluttered and complex scenes, even with
partially filled fluid contents within the vessels. To address the shortcomings
of existing transparent object data collection schemes in literature, we also
propose an automated dataset creation workflow that consists of
robot-controlled image collection and vision-based automatic annotation.
Through this automated workflow, we created Toronto Transparent Objects Depth
Dataset (TODD), which consists of nearly 15000 RGB-D images. Our experimental
evaluation demonstrates that TranspareNet outperforms existing state-of-the-art
depth completion methods on multiple datasets, including ClearGrasp, and that
it also handles cluttered scenes when trained on TODD. Code and dataset will be
released at https://www.pair.toronto.edu/TranspareNet/

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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
35
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: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga