Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 4D-Net for Learned Multi-Modal Alignment   We present 4D-Net, a 3D object detection approach, which utilizes 3D Point
Cloud and RGB sensing information, both in time. We are able to incorporate the
4D information by performing a novel dynamic connection learning across various
feature representations and levels of abstraction, as well as by observing
geometric constraints. Our approach outperforms the state-of-the-art and strong
baselines on the Waymo Open Dataset. 4D-Net is better able to use motion cues
and dense image information to detect distant objects more successfully.

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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con