Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Orientation-boosted Voxel Nets for 3D Object Recognition   Recent work has shown good recognition results in 3D object recognition using
3D convolutional networks. In this paper, we show that the object orientation
plays an important role in 3D recognition. More specifically, we argue that
objects induce different features in the network under rotation. Thus, we
approach the category-level classification task as a multi-task problem, in
which the network is trained to predict the pose of the object in addition to
the class label as a parallel task. We show that this yields significant
improvements in the classification results. We test our suggested architecture
on several datasets representing various 3D data sources: LiDAR data, CAD
models, and RGB-D images. We report state-of-the-art results on classification
as well as significant improvements in precision and speed over the baseline on
3D detection.

17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex