Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PerMO: Perceiving More at Once from a Single Image for Autonomous
  Driving   We present a novel approach to detect, segment, and reconstruct complete
textured 3D models of vehicles from a single image for autonomous driving. Our
approach combines the strengths of deep learning and the elegance of
traditional techniques from part-based deformable model representation to
produce high-quality 3D models in the presence of severe occlusions. We present
a new part-based deformable vehicle model that is used for instance
segmentation and automatically generate a dataset that contains dense
correspondences between 2D images and 3D models. We also present a novel
end-to-end deep neural network to predict dense 2D/3D mapping and highlight its
benefits. Based on the dense mapping, we are able to compute precise 6-DoF
poses and 3D reconstruction results at almost interactive rates on a commodity
GPU. We have integrated these algorithms with an autonomous driving system. In
practice, our method outperforms the state-of-the-art methods for all major
vehicle parsing tasks: 2D instance segmentation by 4.4 points (mAP), 6-DoF pose
estimation by 9.11 points, and 3D detection by 1.37. Moreover, we have released
all of the source code, dataset, and the trained model on Github.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph