Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D-BEVIS: Bird's-Eye-View Instance Segmentation   Recent deep learning models achieve impressive results on 3D scene analysis
tasks by operating directly on unstructured point clouds. A lot of progress was
made in the field of object classification and semantic segmentation. However,
the task of instance segmentation is less explored. In this work, we present
3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on
point clouds. Following the idea of previous proposal-free instance
segmentation approaches, our model learns a feature embedding and groups the
obtained feature space into semantic instances. Current point-based methods
scale linearly with the number of points by processing local sub-parts of a
scene individually. However, to perform instance segmentation by clustering,
globally consistent features are required. Therefore, we propose to combine
local point geometry with global context information from an intermediate
bird's-eye view representation.

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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin