Local region-learning modules for point cloud classification
- URL: http://arxiv.org/abs/2303.17338v2
- Date: Tue, 19 Dec 2023 10:06:08 GMT
- Title: Local region-learning modules for point cloud classification
- Authors: Kaya Turgut and Helin Dutagaci
- Abstract summary: We present two local region-learning modules that infer the appropriate shift for each center point and alter the radius of each local region.
We integrated both modules independently and together to the PointNet++ and PointCNN object classification architectures.
Our experiments on ShapeNet data set showed that the modules are also effective on 3D CAD models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Data organization via forming local regions is an integral part of deep
learning networks that process 3D point clouds in a hierarchical manner. At
each level, the point cloud is sampled to extract representative points and
these points are used to be centers of local regions. The organization of local
regions is of considerable importance since it determines the location and size
of the receptive field at a particular layer of feature aggregation. In this
paper, we present two local region-learning modules: Center Shift Module to
infer the appropriate shift for each center point, and Radius Update Module to
alter the radius of each local region. The parameters of the modules are
learned through optimizing the loss associated with the particular task within
an end-to-end network. We present alternatives for these modules through
various ways of modeling the interactions of the features and locations of 3D
points in the point cloud. We integrated both modules independently and
together to the PointNet++ and PointCNN object classification architectures,
and demonstrated that the modules contributed to a significant increase in
classification accuracy for the ScanObjectNN data set consisting of scans of
real-world objects. Our further experiments on ShapeNet data set showed that
the modules are also effective on 3D CAD models.
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