Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ROCA: Robust CAD Model Retrieval and Alignment from a Single Image   We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD
models from a shape database to a single input image. This enables 3D
perception of an observed scene from a 2D RGB observation, characterized as a
lightweight, compact, clean CAD representation. Core to our approach is our
differentiable alignment optimization based on dense 2D-3D object
correspondences and Procrustes alignment. ROCA can thus provide a robust CAD
alignment while simultaneously informing CAD retrieval by leveraging the 2D-3D
correspondences to learn geometrically similar CAD models. Experiments on
challenging, real-world imagery from ScanNet show that ROCA significantly
improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD
alignment accuracy.

9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con