Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distinctive 3D local deep descriptors   We present a simple but yet effective method for learning distinctive 3D
local deep descriptors (DIPs) that can be used to register point clouds without
requiring an initial alignment. Point cloud patches are extracted,
canonicalised with respect to their estimated local reference frame and encoded
into rotation-invariant compact descriptors by a PointNet-based deep neural
network. DIPs can effectively generalise across different sensor modalities
because they are learnt end-to-end from locally and randomly sampled points.
Because DIPs encode only local geometric information, they are robust to
clutter, occlusions and missing regions. We evaluate and compare DIPs against
alternative hand-crafted and deep descriptors on several indoor and outdoor
datasets consisting of point clouds reconstructed using different sensors.
Results show that DIPs (i) achieve comparable results to the state-of-the-art
on RGB-D indoor scenes (3DMatch dataset), (ii) outperform state-of-the-art by a
large margin on laser-scanner outdoor scenes (ETH dataset), and (iii)
generalise to indoor scenes reconstructed with the Visual-SLAM system of
Android ARCore. Source code: https://github.com/fabiopoiesi/dip.

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The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas