Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo
  Reconstruction   Deep Neural Networks (DNNs) have the potential to improve the quality of
image-based 3D reconstructions. However, the use of DNNs in the context of 3D
reconstruction from large and high-resolution image datasets is still an open
challenge, due to memory and computational constraints. We propose a pipeline
which takes advantage of DNNs to improve the quality of 3D reconstructions
while being able to handle large and high-resolution datasets. In particular,
we propose a confidence prediction network explicitly tailored for Multi-View
Stereo (MVS) and we use it for both depth map outlier filtering and depth map
refinement within our pipeline, in order to improve the quality of the final 3D
reconstructions. We train our confidence prediction network on (semi-)dense
ground truth depth maps from publicly available real world MVS datasets. With
extensive experiments on popular benchmarks, we show that our overall pipeline
can produce state-of-the-art 3D reconstructions, both qualitatively and
quantitatively.

5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th