Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis   This paper proposes an end-to-end learning framework for multiview
stereopsis. We term the network SurfaceNet. It takes a set of images and their
corresponding camera parameters as input and directly infers the 3D model. The
key advantage of the framework is that both photo-consistency as well geometric
relations of the surface structure can be directly learned for the purpose of
multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D
convolutional network which is achieved by encoding the camera parameters
together with the images in a 3D voxel representation. We evaluate SurfaceNet
on the large-scale DTU benchmark.

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The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
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The categories of this paper are: nlin
7
The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph