Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying Unknown Instances for Autonomous Driving   In the past few years, we have seen great progress in perception algorithms,
particular through the use of deep learning. However, most existing approaches
focus on a few categories of interest, which represent only a small fraction of
the potential categories that robots need to handle in the real-world. Thus,
identifying objects from unknown classes remains a challenging yet crucial
task. In this paper, we develop a novel open-set instance segmentation
algorithm for point clouds which can segment objects from both known and
unknown classes in a holistic way. Our method uses a deep convolutional neural
network to project points into a category-agnostic embedding space in which
they can be clustered into instances irrespective of their semantics.
Experiments on two large-scale self-driving datasets validate the effectiveness
of our proposed method.

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The categories of this paper are: math
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The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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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: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: cond-mat