PointManifold: Using Manifold Learning for Point Cloud Classification
- URL: http://arxiv.org/abs/2010.07215v2
- Date: Fri, 16 Oct 2020 06:32:05 GMT
- Title: PointManifold: Using Manifold Learning for Point Cloud Classification
- Authors: Dinghao Yang, Wei Gao
- Abstract summary: We propose a point cloud classification method based on graph neural network and manifold learning.
This paper uses manifold learning algorithms to embed point cloud features for better considering continuity on the surface.
Experiments show that the proposed point cloud classification methods obtain the mean class accuracy (mA) of 90.2% and the overall accuracy (oA) of 93.2%.
- Score: 5.705680763604835
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a point cloud classification method based on graph
neural network and manifold learning. Different from the conventional point
cloud analysis methods, this paper uses manifold learning algorithms to embed
point cloud features for better considering the geometric continuity on the
surface. Then, the nature of point cloud can be acquired in low dimensional
space, and after being concatenated with features in the original
three-dimensional (3D)space, both the capability of feature representation and
the classification network performance can be improved. We pro-pose two
manifold learning modules, where one is based on locally linear embedding
algorithm, and the other is a non-linear projection method based on neural
network architecture. Both of them can obtain better performances than the
state-of-the-art baseline. Afterwards, the graph model is constructed by using
the k nearest neighbors algorithm, where the edge features are effectively
aggregated for the implementation of point cloud classification. Experiments
show that the proposed point cloud classification methods obtain the mean class
accuracy (mA) of 90.2% and the overall accuracy (oA)of 93.2%, which reach
competitive performances compared with the existing state-of-the-art related
methods.
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