Neural Network Point Cloud Shape Conversion via Multi-Scale Displacement
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Solution Overview
Problem
Current methods for processing point cloud data using neural networks cannot implement shape conversion of point clouds, which is essential for transforming geometric shapes of target objects.
Innovation Solution
A method and apparatus that utilize a neural network with structure parameters including a collection abstraction layer, feature propagation layer, and fully connected layer to acquire multi-scale features and displacement vectors, enabling shape transformation of point clouds by processing first and second point set data to obtain target point set data with transformed displacements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional neural network methods (PointNet, PointNet++) are used to process point cloud data, then classification and segmentation can be implemented, but shape conversion of the point cloud cannot be achieved
Solution Approach 1:
The patent segments the point cloud processing into distinct functional modules: a collection abstraction layer that divides points into multiple scales, a feature propagation layer that processes features at each scale, and a fully connected layer that integrates features. This segmentation enables shape conversion by allowing independent processing of geometric features at different scales, which traditional methods cannot achieve.
Solution Approach 2:
The patent introduces multi-scale features as an additional dimension for processing point cloud data. By collecting features at multiple scales (different radii) and propagating them through the network, the system gains the capability to represent and transform shapes in a multi-dimensional feature space, enabling shape conversion that single-scale methods cannot achieve.
2Adaptability or versatility
If multi-scale feature collection is implemented through the neural network, then shape conversion capability is achieved, but computational complexity increases
Solution Approach 1:
The patent designs a universal neural network architecture that can handle multiple tasks (classification, segmentation, and shape conversion) through the same multi-scale feature collection and propagation mechanism. The collection abstraction layer and feature propagation layer serve multiple functions, reducing the need for separate specialized networks for each task and managing computational complexity through shared components.
Solution Approach 2:
The patent implements a nested structure where points are organized into multiple scales with different radii, and features are propagated from inner scales to outer scales. This nested organization allows the network to process features hierarchically, managing complexity by breaking down the shape conversion task into manageable nested layers rather than processing all points simultaneously.
3Manufacturing precision
If displacement vectors are calculated for each point based on multi-scale features, then accurate shape transformation is achieved, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and organization in the collection abstraction layer, where points are grouped into multiple scales and features are collected beforehand. This preliminary action prepares the data structure in advance, allowing the feature propagation layer and fully connected layer to efficiently calculate displacement vectors without redundant computations, thus reducing overall processing time while maintaining accuracy.
Data Source
AI summary
The disclosure relates processing point cloud data based on a neural network. The method includes: acquiring first point set data and second point set data respectively representing a point data set of an outer surface of a target object in different shapes; processing the first point set data using structure parameters of the neural network to obtain a multi-scale feature of each point in the first point set data; acquiring target point set data according to the first displacement vector, the first point set data and the second point set data. The method of processing the point cloud data provided in the present application can implement shape conversion of the point cloud.