Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal
  Transport   Good quality reconstruction and comprehension of a scene rely on 3D
estimation methods. The 3D information was usually obtained from images by
stereo-photogrammetry, but deep learning has recently provided us with
excellent results for monocular depth estimation. Building up a sufficiently
large and rich training dataset to achieve these results requires onerous
processing. In this paper, we address the problem of learning outdoor 3D point
cloud from monocular data using a sparse ground-truth dataset. We propose
Pix2Point, a deep learning-based approach for monocular 3D point cloud
prediction, able to deal with complete and challenging outdoor scenes. Our
method relies on a 2D-3D hybrid neural network architecture, and a supervised
end-to-end minimisation of an optimal transport divergence between point
clouds. We show that, when trained on sparse point clouds, our simple promising
approach achieves a better coverage of 3D outdoor scenes than efficient
monocular depth methods.

11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin