Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D-GMNet: Single-View 3D Shape Recovery as A Gaussian Mixture   In this paper, we introduce 3D-GMNet, a deep neural network for 3D object
shape reconstruction from a single image. As the name suggests, 3D-GMNet
recovers 3D shape as a Gaussian mixture. In contrast to voxels, point clouds,
or meshes, a Gaussian mixture representation provides an analytical expression
with a small memory footprint while accurately representing the target 3D
shape. At the same time, it offers a number of additional advantages including
instant pose estimation and controllable level-of-detail reconstruction, while
also enabling interpretation as a point cloud, volume, and a mesh model. We
train 3D-GMNet end-to-end with single input images and corresponding 3D models
by introducing two novel loss functions, a 3D Gaussian mixture loss and a 2D
multi-view loss, which collectively enable accurate shape reconstruction as
kernel density estimation. We thoroughly evaluate the effectiveness of 3D-GMNet
with synthetic and real images of objects. The results show accurate
reconstruction with a compact representation that also realizes novel
applications of single-image 3D reconstruction.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
16
The categories of this paper are: stat
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom