Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image
  Datasets via RGBD Image Synthesis   Understanding three-dimensional (3D) geometries from two-dimensional (2D)
images without any labeled information is promising for understanding the real
world without incurring annotation cost. We herein propose a novel generative
model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D
images. The proposed method enables camera parameter-conditional image
generation and depth image generation without any 3D annotations, such as
camera poses or depth. We use an explicit 3D consistency loss for two RGBD
images generated from different camera parameters, in addition to the ordinal
GAN objective. The loss is simple yet effective for any type of image generator
such as DCGAN and StyleGAN to be conditioned on camera parameters. Through
experiments, we demonstrated that the proposed method could learn 3D
representations from 2D images with various generator architectures.

6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex