3D Point Cloud Pose Annotation for Low-Light Human Detection
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Solution Overview
Problem
Current pose estimation techniques using camera sensors face challenges in low-light environments and struggle to accurately determine the relative positions of individuals without depth information, limiting their effectiveness in autonomous driving and other applications.
Innovation Solution
The use of LiDAR sensors to generate point cloud data, which is processed using a deep neural network with a combination of 3D and 2D convolutional layers to detect persons and estimate their poses, enabling accurate and rapid pose estimation even in difficult environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera sensors are used for pose estimation, then the system is simpler and cheaper, but it performs poorly in low-light environments and lacks depth information
Solution Approach 1:
The patent combines LiDAR depth sensing with RGB camera imaging into an integrated sensor system. The LiDAR sensor array captures depth information while the RGB camera captures color information, and both are processed together by the neural network to achieve reliable pose estimation in low-light conditions where either sensor alone would be insufficient
Solution Approach 2:
The patent transitions from 2D image data from cameras to 3D point cloud data from LiDAR sensors. This addition of the depth dimension (Z-axis) provides spatial information that enables accurate pose estimation independent of lighting conditions, resolving the contradiction between reliability in low-light and device complexity
2Measurement precision
If LiDAR sensors are used to generate point cloud data, then depth information and low-light performance are improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent introduces voxelization as an intermediary processing step that converts raw LiDAR point cloud data into a structured 3D grid representation. This voxelized format serves as a mediator between the unstructured point cloud and the neural network, simplifying the data structure while preserving depth information and making it more computationally efficient to process
Solution Approach 2:
The patent replaces traditional computer vision processing pipelines with a deep learning-based neural network that directly processes voxelized point cloud data. This substitution of mechanical/image-processing algorithms with neural network-based processing enables the system to handle the complexity of LiDAR data while achieving accurate pose estimation
3Measurement precision
If 3D convolutional layers are used in the neural network, then pose estimation accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent segments the neural network processing into distinct stages: first applying 3D convolutional layers to the voxelized point cloud for accurate spatial feature extraction, then transitioning to 2D convolutional layers for faster processing of the extracted features. This segmentation allows the system to achieve high accuracy in the critical depth processing stage while maintaining overall processing speed through the use of computationally lighter 2D operations in subsequent stages
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate and efficient pose estimation in various environments, including low-light conditions, by leveraging the depth information from LiDAR sensors and combining it with the processing power of deep neural networks, enhancing the reliability of autonomous driving systems and other applications.
Implementation Method 1
receive point cloud data from a LiDAR sensor or other similar sensor
Implementation Method 2
a LiDAR sensor or other similar sensor
Data Source
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AI summary
A method and apparatus for annotating point cloud data. An apparatus may be configured to cause display of the point cloud data, label points in the point cloud data with a plurality of annotation points, the plurality of annotation points corresponding to points on a human body, move, in response to a user input, one or more of the annotation points to define a human pose and create annotated point cloud data, and output the annotated point cloud data.