Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D Object Detection Combining Semantic and Geometric Features from Point
  Clouds   In this paper, we investigate the combination of voxel-based methods and
point-based methods, and propose a novel end-to-end two-stage 3D object
detector named SGNet for point clouds scenes. The voxel-based methods voxelize
the scene to regular grids, which can be processed with the current advanced
feature learning frameworks based on convolutional layers for semantic feature
learning. Whereas the point-based methods can better extract the geometric
feature of the point due to the coordinate reservations. The combination of the
two is an effective solution for 3D object detection from point clouds.
However, most current methods use a voxel-based detection head with anchors for
final classification and localization. Although the preset anchors cover the
entire scene, it is not suitable for point clouds detection tasks with larger
scenes and multiple categories due to the limitation of voxel size. In this
paper, we propose a voxel-to-point module (VTPM) that captures semantic and
geometric features. The VTPM is a Voxel-Point-Based Module that finally
implements 3D object detection in point space, which is more conducive to the
detection of small-size objects and avoids the presets of anchors in inference
stage. In addition, a Confidence Adjustment Module (CAM) with the
center-boundary-aware confidence attention is proposed to solve the
misalignment between the predicted confidence and proposals in the regions of
the interest (RoI) selection. The SGNet proposed in this paper has achieved
state-of-the-art results for 3D object detection in the KITTI dataset,
especially in the detection of small-size objects such as cyclists. Actually,
as of September 19, 2021, for KITTI dataset, SGNet ranked 1st in 3D and BEV
detection on cyclists with easy difficulty level, and 2nd in the 3D detection
of moderate cyclists.

22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math