Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D Object Proposals using Stereo Imagery for Accurate Object Class
  Detection   The goal of this paper is to perform 3D object detection in the context of
autonomous driving. Our method first aims at generating a set of high-quality
3D object proposals by exploiting stereo imagery. We formulate the problem as
minimizing an energy function that encodes object size priors, placement of
objects on the ground plane as well as several depth informed features that
reason about free space, point cloud densities and distance to the ground. We
then exploit a CNN on top of these proposals to perform object detection. In
particular, we employ a convolutional neural net (CNN) that exploits context
and depth information to jointly regress to 3D bounding box coordinates and
object pose. Our experiments show significant performance gains over existing
RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When
combined with the CNN, our approach outperforms all existing results in object
detection and orientation estimation tasks for all three KITTI object classes.
Furthermore, we experiment also with the setting where LIDAR information is
available, and show that using both LIDAR and stereo leads to the best result.

4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc