Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Segment 3D Point Clouds in 2D Image Space   In contrast to the literature where local patterns in 3D point clouds are
captured by customized convolutional operators, in this paper we study the
problem of how to effectively and efficiently project such point clouds into a
2D image space so that traditional 2D convolutional neural networks (CNNs) such
as U-Net can be applied for segmentation. To this end, we are motivated by
graph drawing and reformulate it as an integer programming problem to learn the
topology-preserving graph-to-grid mapping for each individual point cloud. To
accelerate the computation in practice, we further propose a novel hierarchical
approximate algorithm. With the help of the Delaunay triangulation for graph
construction from point clouds and a multi-scale U-Net for segmentation, we
manage to demonstrate the state-of-the-art performance on ShapeNet and PartNet,
respectively, with significant improvement over the literature. Code is
available at https://github.com/Zhang-VISLab.

16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
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
23
The categories of this paper are: solv-int