Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Inverse Rendering of an Indoor Scene from a Single Image   Inverse rendering aims to estimate physical attributes of a scene, e.g.,
reflectance, geometry, and lighting, from image(s). Inverse rendering has been
studied primarily for single objects or with methods that solve for only one of
the scene attributes. We propose the first learning-based approach that jointly
estimates albedo, normals, and lighting of an indoor scene from a single image.
Our key contribution is the Residual Appearance Renderer (RAR), which can be
trained to synthesize complex appearance effects (e.g., inter-reflection, cast
shadows, near-field illumination, and realistic shading), which would be
neglected otherwise. This enables us to perform self-supervised learning on
real data using a reconstruction loss, based on re-synthesizing the input image
from the estimated components. We finetune with real data after pretraining
with synthetic data. To this end, we use physically-based rendering to create a
large-scale synthetic dataset, which is a significant improvement over prior
datasets. Experimental results show that our approach outperforms
state-of-the-art methods that estimate one or more scene attributes.

30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci