Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution
Neural Network
- URL: http://arxiv.org/abs/2205.01550v2
- Date: Wed, 4 May 2022 06:54:35 GMT
- Title: Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution
Neural Network
- Authors: Yunzheng Su
- Abstract summary: We propose a feature extraction module based on multi-scale ultra-sparse convolution and a feature selection module based on channel attention.
By introducing multi-scale sparse convolution, network could capture richer feature information based on convolution kernels of different sizes.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Point clouds have the characteristics of disorder, unstructured and
sparseness.Aiming at the problem of the non-structural nature of point clouds,
thanks to the excellent performance of convolutional neural networks in image
processing, one of the solutions is to extract features from point clouds based
on two-dimensional convolutional neural networks. The three-dimensional
information carried in the point cloud can be converted to two-dimensional, and
then processed by a two-dimensional convolutional neural network, and finally
back-projected to three-dimensional.In the process of projecting 3D information
to 2D and back-projection, certain information loss will inevitably be caused
to the point cloud and category inconsistency will be introduced in the
back-projection stage;Another solution is the voxel-based point cloud
segmentation method, which divides the point cloud into small grids one by
one.However, the point cloud is sparse, and the direct use of 3D convolutional
neural network inevitably wastes computing resources. In this paper, we propose
a feature extraction module based on multi-scale ultra-sparse convolution and a
feature selection module based on channel attention, and build a point cloud
segmentation network framework based on this.By introducing multi-scale sparse
convolution, network could capture richer feature information based on
convolution kernels of different sizes, improving the segmentation result of
point cloud segmentation.
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