Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fusing Local Similarities for Retrieval-based 3D Orientation Estimation
  of Unseen Objects   In this paper, we tackle the task of estimating the 3D orientation of
previously-unseen objects from monocular images. This task contrasts with the
one considered by most existing deep learning methods which typically assume
that the testing objects have been observed during training. To handle the
unseen objects, we follow a retrieval-based strategy and prevent the network
from learning object-specific features by computing multi-scale local
similarities between the query image and synthetically-generated reference
images. We then introduce an adaptive fusion module that robustly aggregates
the local similarities into a global similarity score of pairwise images.
Furthermore, we speed up the retrieval process by developing a fast
clustering-based retrieval strategy. Our experiments on the LineMOD,
LineMOD-Occluded, and T-LESS datasets show that our method yields a
significantly better generalization to unseen objects than previous works.

15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg