Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection
  from point clouds   Object detection in point cloud data is one of the key components in computer
vision systems, especially for autonomous driving applications. In this work,
we present Voxel-FPN, a novel one-stage 3D object detector that utilizes raw
data from LIDAR sensors only. The core framework consists of an encoder network
and a corresponding decoder followed by a region proposal network. Encoder
extracts multi-scale voxel information in a bottom-up manner while decoder
fuses multiple feature maps from various scales in a top-down way. Extensive
experiments show that the proposed method has better performance on extracting
features from point data and demonstrates its superiority over some baselines
on the challenging KITTI-3D benchmark, obtaining good performance on both speed
and accuracy in real-world scenarios.

18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex