Point Cloud Segmentation Using Spatially Ordered 1D Convolution
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Solution Overview
Problem
Existing methods for semantic segmentation of point clouds in driver assistance systems are inefficient in terms of computational and memory requirements, particularly for sparse data from radar sensors, due to quadratic or cubic scaling with grid-based approaches and complex algorithms for graph-based representations.
Innovation Solution
Reorder points in a point cloud to form a one-dimensional sequence where the ordinal number difference correlates with spatial distance, allowing for one-dimensional convolution operations, reducing memory and computational load, and achieve translation invariance by restricting weight matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If grid-based approaches are used for semantic segmentation of point clouds, then convolution operations can be performed to recognize structures, but computational effort and memory requirements scale quadratically or cubically with the number of grid cells
Solution Approach 1:
The method segments the point cloud data by ordering points to form a one-dimensional sequence, where each point's neighborhood is defined by a fixed number of adjacent points in the sequence. This segmentation approach replaces the grid-based segmentation with a sequence-based segmentation that reduces computational complexity while maintaining the ability to recognize local structures through convolution operations.
Solution Approach 2:
The invention transforms the multi-dimensional point cloud data into a one-dimensional sequence representation. By ordering points along a one-dimensional axis and defining neighborhoods based on sequential position rather than spatial grid coordinates, the method reduces the dimensional complexity of convolution operations from quadratic/cubic to linear scaling.
2Productivity
If graph-based neural networks are used for convolution operations on point clouds, then computational effort scales with the number of points, but complex algorithms are required to create meaningful graph representations
Solution Approach 1:
The method extracts the essential neighborhood relationship information from complex graph structures by representing neighborhoods simply as contiguous sequences of points. Instead of maintaining full graph representations with edges and nodes, the invention extracts only the sequential ordering and fixed-size window information needed for convolution, eliminating the complexity of graph construction algorithms.
Solution Approach 2:
The invention changes the parameter representation from graph-theoretic concepts (nodes, edges, adjacency matrices) to simple sequential parameters (ordinal numbers, fixed window sizes). This parameter transformation maintains the computational efficiency benefits of graph-based methods while eliminating the algorithmic complexity of graph construction and manipulation.
3Productivity
If point clouds are reordered to form a one-dimensional sequence for efficient convolution, then computational and memory requirements are reduced, but the spatial relationship representation must be preserved
Solution Approach 1:
The method performs preliminary ordering of points to create a one-dimensional sequence that preserves spatial relationships. By carefully arranging points in sequential order based on their spatial coordinates before applying convolution operations, the invention ensures that neighboring points in the sequence correspond to spatially adjacent points, thereby preserving spatial relationship information in the reordered representation.
Data Source
AI summary
A method for the semantic segmentation of a point cloud by a neural network in a driver assistance system for motor vehicles, in which a neighborhood is defined for each individual point of the point cloud, the neighborhood being a set of other points of the point cloud located in the vicinity of the point, and in which a feature of an individual point is convolved with features of the points in its neighborhood according to a learned weight matrix. The points of the point cloud are ordered to form a sequence by assigning each point an ordinal number which indicates its position in the sequence. An algorithm is used to create the sequence. The algorithm ensures that the difference between the ordinal numbers of any two points correlates positively with the spatial distance of these points in the point cloud.