Multi-Resolution Voxel Segmentation for Fast 3D Object Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately segmenting three-dimensional sensor data due to resource constraints, leading to inaccurate object detection and poor responsiveness, especially when increasing voxel space size and number, which demands significant memory and processing resources.
Innovation Solution
Implementing a multi-resolution voxel space with nested voxel structures and machine learning algorithms to process sensor data, aligning high-resolution data near the vehicle with lower-resolution data further away, and using top-down segmentation techniques to enhance object detection accuracy and reduce memory and processing demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the voxel space size and number of voxels are increased to improve segmentation accuracy, then object detection accuracy is improved, but memory consumption and processing resources rapidly increase
Solution Approach 1:
The patent divides the three-dimensional sensor data into multiple two-dimensional bird's-eye view images representing different depth ranges. Each image is processed independently through the neural network, allowing accurate segmentation without requiring a single large-scale voxel space. This segmentation approach maintains high detection accuracy while significantly reducing memory consumption compared to processing the entire scene at once.
Solution Approach 2:
The patent transforms three-dimensional sensor data into two-dimensional bird's-eye view images by projecting data along the vertical dimension. This dimensionality reduction converts complex 3D voxel processing into simpler 2D image processing, maintaining segmentation accuracy while reducing computational resources and memory requirements.
2Measurement precision
If the voxel space size and number of voxels are increased to improve segmentation accuracy, then object detection accuracy is improved, but processing time increases
Solution Approach 1:
By dividing the scene into multiple bird's-eye view images representing different depth ranges, the patent enables parallel processing of smaller data subsets. Each image is processed independently and quickly through the neural network, reducing overall processing time compared to processing a single large-scale voxel space while maintaining comprehensive object detection accuracy.
Solution Approach 2:
The transformation from 3D voxel data to 2D bird's-eye view images simplifies the computational complexity. The neural network processes 2D images more efficiently than 3D voxel data, reducing processing time while preserving the essential spatial relationships needed for accurate object detection and segmentation.
3Measurement precision
If high-resolution data is used for all ranges to improve detection accuracy, then segmentation precision is improved, but resource requirements increase significantly
Solution Approach 1:
The patent creates multiple bird's-eye view images with different resolutions optimized for specific depth ranges. Near-range objects are represented with higher resolution in dedicated images, while far-range objects use appropriately scaled resolution. This local optimization ensures high segmentation precision for all objects while avoiding the waste of processing resources that would occur if maximum resolution were applied uniformly across all ranges.
Data Source
AI summary
Techniques for segmenting sensor data are discussed herein. Data can be represented in individual levels in a multi-resolution voxel space. A first level can correspond to a first region of an environment and a second level can correspond to a second region of an environment that is a subset of the first region. In some examples, the levels can comprise a same number of voxels, such that the first level covers a large, low-resolution region, while the second level covers a smaller, higher-resolution region, though more levels are contemplated. Operations may include analyzing sensor data represented in the voxel space from a perspective, such as a top-down perspective. From this perspective, techniques may generate masks that represent objects in the voxel space. Additionally, techniques may generate segmentation data to verify and/or generate the masks, or otherwise cluster the sensor data.