Point Cloud Processing via Hausdorff Convolution for Semantic Classification
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Solution Overview
Problem
Conventional point cloud data processing methods result in sparse data, leading to lower accuracy in semantic classification due to regular sub-sampling and regularization at lower resolutions.
Innovation Solution
A method involving the acquisition of point cloud data, construction of neighboring point sets, calculation of Hausdorff distances with a kernel point cloud, convolution in a Hausdorff convolution layer, and feature dimension reduction using a deep learning network to enhance semantic classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If regular sub-sampling is performed on point cloud data, then processing complexity is reduced, but data sparsity increases leading to lower classification accuracy
Solution Approach 1:
The patent transforms point cloud data from 3D spatial coordinates into 2D image representations through projection transformations. This parameter change in data representation allows the application of mature 2D convolutional neural network techniques while preserving essential spatial relationships, thereby maintaining classification accuracy without requiring complex 3D processing operations.
Solution Approach 2:
The patent replaces complex 3D point cloud processing mechanisms with 2D image processing mechanisms. By projecting 3D points onto 2D planes and using 2D convolution operations instead of 3D operations, the system achieves lower computational complexity while maintaining effective feature extraction capabilities through the preserved spatial relationships in the projected view.
2Speed
If point cloud data is regularized at lower resolution, then processing speed increases, but feature extraction accuracy deteriorates
Solution Approach 1:
The patent projects 3D point cloud data onto 2D planes, creating a dimensional transformation that simplifies the data structure for faster processing. This 3D-to-2D projection maintains essential spatial relationships while reducing computational complexity, enabling efficient processing without significant loss of feature extraction accuracy.
3Device complexity
If conventional sub-sampling is applied, then data processing becomes simpler, but information loss increases
Solution Approach 1:
The patent creates 2D image copies or projections of the 3D point cloud data. These 2D representations serve as informative copies that preserve essential spatial relationships and structural features while being much simpler to process. The projection process captures key geometric information in a compressed 2D format, minimizing information loss relative to the original 3D data.
Data Source
AI summary
A point cloud data processing method and device, a computer device and a storage medium are provided. The method includes: acquiring point cloud data, and constructing a corresponding neighboring point set for each of data points in the point cloud data; calculating Hausdorff distances between the neighboring point set and a pre-constructed kernel point cloud to obtain a distance matrix; calculating a convolution of the neighboring point set with the distance matrix and a network weight matrix in a Hausdorff convolution layer in an encoder, to obtain high-dimensional point cloud features, the encoder and a decoder being two parts in a deep learning network; and reducing feature dimension of the high-dimensional point cloud features through the decoder, so that a classifier performs semantic classification on the point cloud data according to object point cloud features obtained by the dimension reduction.