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(I Introduction)
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<< /D (section.2) /S /GoTo >>
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(II Related Work)
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<< /D (section.3) /S /GoTo >>
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(III Multi-objective framework with geometry-aware representation)
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<< /D (subsection.3.1) /S /GoTo >>
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(III-A Learning generative geometry-aware representation from RGBD input)
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<< /D (subsection.3.2) /S /GoTo >>
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(III-B Depth supervision with in-network projection layer)
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<< /D (subsection.3.3) /S /GoTo >>
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(III-C Viewpoint-invariant geometry-aware representation with multi-view supervision)
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<< /D (subsection.3.4) /S /GoTo >>
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(III-D Learning predictive grasping interaction with geometry-aware representation.)
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<< /D (subsection.3.5) /S /GoTo >>
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(III-E DGGN: Deep geometry-aware grasping network. )
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<< /D (section.4) /S /GoTo >>
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(IV Experiments)
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<< /D (subsection.4.1) /S /GoTo >>
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(IV-A Dataset collection)
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<< /D (subsection.4.2) /S /GoTo >>
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(IV-B Implementation details)
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<< /D (subsection.4.3) /S /GoTo >>
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(IV-C Visualization: 3D shape generation)
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<< /D (subsection.4.4) /S /GoTo >>
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(IV-D Model evaluation: Grasping outcome prediction)
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<< /D (subsection.4.5) /S /GoTo >>
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(IV-E Application: Analysis-by-synthesis grasping planning.)
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<< /D (section.5) /S /GoTo >>
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(V Conclusions and Future Work)
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<< /D (section*.2) /S /GoTo >>
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(References)
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