Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: InSeGAN: A Generative Approach to Segmenting Identical Instances in
  Depth Images   In this paper, we present InSeGAN, an unsupervised 3D generative adversarial
network (GAN) for segmenting (nearly) identical instances of rigid objects in
depth images. Using an analysis-by-synthesis approach, we design a novel GAN
architecture to synthesize a multiple-instance depth image with independent
control over each instance. InSeGAN takes in a set of code vectors (e.g.,
random noise vectors), each encoding the 3D pose of an object that is
represented by a learned implicit object template. The generator has two
distinct modules. The first module, the instance feature generator, uses each
encoded pose to transform the implicit template into a feature map
representation of each object instance. The second module, the depth image
renderer, aggregates all of the single-instance feature maps output by the
first module and generates a multiple-instance depth image. A discriminator
distinguishes the generated multiple-instance depth images from the
distribution of true depth images. To use our model for instance segmentation,
we propose an instance pose encoder that learns to take in a generated depth
image and reproduce the pose code vectors for all of the object instances. To
evaluate our approach, we introduce a new synthetic dataset, "Insta-10",
consisting of 100,000 depth images, each with 5 instances of an object from one
of 10 classes. Our experiments on Insta-10, as well as on real-world noisy
depth images, show that InSeGAN achieves state-of-the-art performance, often
outperforming prior methods by large margins.

10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat