Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point
  Cloud Reconstruction from a Single Image   3D reconstruction from single view images is an ill-posed problem. Inferring
the hidden regions from self-occluded images is both challenging and ambiguous.
We propose a two-pronged approach to address these issues. To better
incorporate the data prior and generate meaningful reconstructions, we propose
3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first
train a 3D point cloud auto-encoder and then learn a mapping from the 2D image
to the corresponding learnt embedding. To tackle the issue of uncertainty in
the reconstruction, we predict multiple reconstructions that are consistent
with the input view. This is achieved by learning a probablistic latent space
with a novel view-specific diversity loss. Thorough quantitative and
qualitative analysis is performed to highlight the significance of the proposed
approach. We outperform state-of-the-art approaches on the task of single-view
3D reconstruction on both real and synthetic datasets while generating multiple
plausible reconstructions, demonstrating the generalizability and utility of
our approach.

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The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
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
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys