Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans   We present SPSG, a novel approach to generate high-quality, colored 3D models
of scenes from RGB-D scan observations by learning to infer unobserved scene
geometry and color in a self-supervised fashion. Our self-supervised approach
learns to jointly inpaint geometry and color by correlating an incomplete RGB-D
scan with a more complete version of that scan. Notably, rather than relying on
3D reconstruction losses to inform our 3D geometry and color reconstruction, we
propose adversarial and perceptual losses operating on 2D renderings in order
to achieve high-resolution, high-quality colored reconstructions of scenes.
This exploits the high-resolution, self-consistent signal from individual raw
RGB-D frames, in contrast to fused 3D reconstructions of the frames which
exhibit inconsistencies from view-dependent effects, such as color balancing or
pose inconsistencies. Thus, by informing our 3D scene generation directly
through 2D signal, we produce high-quality colored reconstructions of 3D
scenes, outperforming state of the art on both synthetic and real data.

27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg