3D Point Cloud Detection Network for Vertically Stacked Objects
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Solution Overview
Problem
Existing deep neural network architectures for object detection in point clouds, such as LiDAR or RADAR, are unable to effectively detect vertically stacked objects, which are crucial for generating high-definition maps and detecting map deviations in autonomous driving environments.
Innovation Solution
A network architecture comprising an encoder, backbone, and head with three-dimensional convolution components processes point clouds within a three-dimensional voxel grid, enabling the detection of vertically stacked objects like traffic signs by utilizing a three-dimensional anchor grid and performing three-dimensional convolutions in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current deep neural network architectures focus on objects on the ground plane only, then the detection capability for movable parts is improved, but the capability to detect vertically stacked objects deteriorates
Solution Approach 1:
The patent transitions from 2D ground-plane detection to 3D voxel grid processing. The network architecture processes point cloud data by discretizing space into 3D voxels, enabling detection of objects at different heights and vertical positions. This dimensional expansion allows the system to detect vertically stacked objects while maintaining ground-plane object detection capabilities.
2Measurement precision
If 3D convolution components are introduced to process point cloud data in 3D voxel grid, then the detection of vertically stacked objects is improved, but the computational complexity increases
Solution Approach 1:
The network architecture is segmented into distinct functional components: encoder for point cloud processing, backbone for feature extraction using 3D convolutions, and head for detection and regression. This segmentation allows selective application of computationally intensive 3D convolution operations only in the backbone where they are most beneficial, while keeping other parts more efficient.
Solution Approach 2:
The patent applies 3D convolution operations selectively in specific regions of the network architecture (backbone) where they provide the most value for vertical object detection. The encoder and head use different processing strategies, optimizing the balance between computational complexity and detection accuracy for vertically stacked objects.
Data Source
AI summary
A network architecture for a three-dimensional object detection in a point cloud to a method and to vehicle. The network architecture for a three-dimensional object detection in a point cloud may include an encoder, a backbone, and a head, wherein the backbone and/or the head may include a three-dimensional convolution component for processing three-dimensional data of the point cloud within a three-dimensional voxel grid.