Point Cloud Object Detection with Selective Point-Based Processing
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Solution Overview
Problem
Existing methods for object detection in point clouds, particularly for autonomous vehicles, are computationally intensive and fail to leverage the unique features of point cloud data, leading to inefficiencies in processing and latency issues.
Innovation Solution
A non-convolutional point-based neural network system that processes point cloud data by determining two-dimensional proposal locations, generating feature representations, and using an object detection neural network to identify objects in the scene, adaptable to computational resources and latency requirements without retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional image processing techniques are used to detect objects in point clouds, then object detection accuracy can be improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent replaces convolutional image processing techniques (mechanical/systematic approach) with a point-based neural network approach that is specifically designed for point cloud data. This substitution maintains object detection accuracy while significantly reducing computational complexity by adapting the processing method to the native format of the data rather than forcing it into an image-based framework
Solution Approach 2:
The patent changes the fundamental parameters of the detection system by using point-based features and coordinates directly from the point cloud data, rather than transforming the data into image format first. This parameter change allows the system to work natively with point cloud characteristics, reducing the need for computationally intensive transformations while maintaining detection accuracy
2Reliability
If all spatial positions in the point cloud are processed equally using convolutional techniques, then comprehensive object detection is achieved, but processing efficiency decreases
Solution Approach 1:
The patent applies local quality by differentiating processing based on spatial regions - proposal locations with higher object likelihood receive more computational attention through targeted feature generation and processing, while other regions receive minimal processing. This selective approach maintains comprehensive detection capability while improving overall processing efficiency by avoiding uniform computation across all space
3Device complexity
If fixed computational resources are allocated to point cloud processing, then system simplicity is maintained, but adaptability to different latency requirements is reduced
Solution Approach 1:
The patent implements dynamics by making the computational process adaptive - the point-based network can dynamically adjust the number of proposals generated and the depth of feature processing based on latency requirements and computational availability. This allows the same system to meet different real-time performance requirements without requiring separate systems or complex reconfiguration
Solution Approach 2:
The patent applies partial action by generating a limited number of proposals rather than processing all possible locations, and by selectively generating features only for proposal locations that are likely to contain objects. This partial processing approach maintains system simplicity while providing adaptability to latency constraints by adjusting the extent of processing based on requirements
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data representing a sensor measurement of a scene captured by one or more sensors to generate an object detection output that identifies locations of one or more objects in the scene. When deployed within an on-board system of a vehicle, the object detection output that is generated can be used to make autonomous driving decisions for the vehicle with enhanced accuracy.