Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Local Part-Aware Point Cloud Denoising   This paper presents a novel non-local part-aware deep neural network to
denoise point clouds by exploring the inherent non-local self-similarity in 3D
objects and scenes. Different from existing works that explore small local
patches, we design the non-local learning unit (NLU) customized with a graph
attention module to adaptively capture non-local semantically-related features
over the entire point cloud. To enhance the denoising performance, we cascade a
series of NLUs to progressively distill the noise features from the noisy
inputs. Further, besides the conventional surface reconstruction loss, we
formulate a semantic part loss to regularize the predictions towards the
relevant parts and enable denoising in a part-aware manner. Lastly, we
performed extensive experiments to evaluate our method, both quantitatively and
qualitatively, and demonstrate its superiority over the state-of-the-arts on
both synthetic and real-scanned noisy inputs.

31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph