Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Object-Centric Photometric Bundle Adjustment with Deep Shape Prior   Reconstructing 3D shapes from a sequence of images has long been a problem of
interest in computer vision. Classical Structure from Motion (SfM) methods have
attempted to solve this problem through projected point displacement \& bundle
adjustment. More recently, deep methods have attempted to solve this problem by
directly learning a relationship between geometry and appearance. There is,
however, a significant gap between these two strategies. SfM tackles the
problem from purely a geometric perspective, taking no account of the object
shape prior. Modern deep methods more often throw away geometric constraints
altogether, rendering the results unreliable. In this paper we make an effort
to bring these two seemingly disparate strategies together. We introduce
learned shape prior in the form of deep shape generators into Photometric
Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by
the shape prior within the optimization-based inference framework,
demonstrating impressive results.

17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom