Top-Down Multi-Resolution Voxel Segmentation for 3D Sensing
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Solution Overview
Problem
Autonomous vehicles face challenges in accurate object detection and segmentation due to the complexity of three-dimensional data processing in resource-constrained environments, particularly with increasing voxel sizes and numbers leading to memory and processing demands.
Innovation Solution
The implementation of a multi-resolution voxel space with machine learning algorithms for top-down segmentation, where sensor data is represented in multiple voxel layers with varying resolutions, allowing for efficient data management near the vehicle and reducing memory usage by aligning high-resolution data near the sensor and lower-resolution data further away, using techniques like downsampling and padding for accurate processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the voxel space size and number of voxels are increased to improve object detection accuracy, then measurement precision is improved, but memory usage and processing requirements increase
Solution Approach 1:
The patent divides the three-dimensional voxel space into multiple two-dimensional image layers stacked together, where each layer represents a different depth range. This segmentation allows the system to process each layer independently with appropriate resolution, reducing overall memory requirements while maintaining detection accuracy for objects at various distances.
Solution Approach 2:
The patent applies different resolution levels to different regions of the voxel space. High-resolution voxels are used near the vehicle where detailed object detection is critical, while lower-resolution voxels are used further away where less detail is needed. This local quality approach maintains measurement precision for nearby objects while reducing the quantity of high-resolution data stored.
2Measurement precision
If the voxel space size and number of voxels are increased to improve object detection accuracy, then measurement precision is improved, but processing time increases
Solution Approach 1:
By segmenting the 3D voxel space into multiple 2D image layers, the patent enables parallel processing of each layer independently. This segmentation reduces the computational complexity of processing the entire volume at once, thereby reducing processing time while maintaining the ability to detect objects accurately across different depth ranges.
Solution Approach 2:
The patent processes different depth layers at different resolution levels, applying computationally intensive algorithms only where high precision is needed (near-field layers) and using simpler processing for distant layers. This local quality approach optimizes processing time by avoiding unnecessary high-resolution processing throughout the entire voxel space.
3Measurement precision
If high-resolution data is maintained throughout the entire voxel space, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements local quality by assigning different resolution levels to different spatial regions and depth layers. High-resolution data structures are maintained only where needed (near-field regions), while lower-resolution structures are used in distant regions. This reduces device complexity by avoiding the need to manage uniformly high-resolution data throughout the entire voxel space.
Solution Approach 2:
The patent transforms the traditional three-dimensional voxel grid into a multi-layered two-dimensional image representation, where the third dimension (depth) is represented as stacked 2D layers. This dimensional transformation simplifies data management by allowing independent processing and storage of each 2D layer, reducing the overall complexity of managing three-dimensional high-resolution data.
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.