Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ClusterNet: 3D Instance Segmentation in RGB-D Images   We propose a method for instance-level segmentation that uses RGB-D data as
input and provides detailed information about the location, geometry and number
of individual objects in the scene. This level of understanding is fundamental
for autonomous robots. It enables safe and robust decision-making under the
large uncertainty of the real-world. In our model, we propose to use the first
and second order moments of the object occupancy function to represent an
object instance. We train an hourglass Deep Neural Network (DNN) where each
pixel in the output votes for the 3D position of the corresponding object
center and for the object's size and pose. The final instance segmentation is
achieved through clustering in the space of moments. The object-centric
training loss is defined on the output of the clustering. Our method
outperforms the state-of-the-art instance segmentation method on our
synthesized dataset. We show that our method generalizes well on real-world
data achieving visually better segmentation results.

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The categories of this paper are: q-alg
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn