Cylindrical Projection for BEV Depth Estimation
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Solution Overview
Problem
Existing vehicle sensing systems face computational challenges in processing multiple camera inputs, particularly when transforming images into top-down views, which can lead to high memory usage and processing demands, especially with high-resolution images, making real-time processing difficult for autonomous driving applications.
Innovation Solution
The use of cylindrical projections to simplify the 2D-to-3D correspondence, reducing computational complexity and memory usage by maintaining vertical orientations of depicted objects, allowing for more efficient processing and accurate object detection across wide fields of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense pixel-wise depth estimation is performed for BEV generation, then accurate environment representation is achieved, but computational complexity and memory usage increase significantly
Solution Approach 1:
The patent segments the image processing task by treating each vertical column of pixels independently. Instead of processing the entire image as a unified dense pixel-wise depth estimation problem, the method divides the image into multiple vertical strips, where each strip is processed separately to generate depth information. This segmentation reduces the computational burden and memory requirements while maintaining depth estimation accuracy within each column.
Solution Approach 2:
The patent extracts and processes only the essential vertical column information needed for BEV generation, rather than processing all pixel data. By focusing on vertical columns and extracting depth information specifically along these paths, the method eliminates redundant computations and reduces the data volume that needs to be processed, thereby lowering computational complexity while preserving measurement precision.
2Measurement precision
If high-resolution images are used for BEV generation, then object detection accuracy improves, but memory usage and processing demands increase
Solution Approach 1:
The patent extracts only the necessary vertical column data from high-resolution images, processing a reduced subset of pixel information. By taking out and processing only the vertical column elements that contribute to BEV generation, the method maintains object detection accuracy while significantly reducing memory usage and processing demands associated with handling complete high-resolution images.
Solution Approach 2:
The patent segments high-resolution images into vertical columns, processing each column independently rather than handling the entire high-resolution image at once. This segmentation approach allows the system to work with smaller data units, reducing peak memory requirements while preserving the detailed information needed for accurate object detection in the original high-resolution input.
3Area of stationary object
If multiple camera inputs are processed simultaneously, then comprehensive environment coverage is achieved, but processing time increases
Solution Approach 1:
The patent merges the processing of multiple camera inputs by aligning and processing vertical columns from different cameras in a unified framework. Instead of processing each camera input separately through independent pipelines, the method combines the vertical column data from multiple cameras and processes them together, achieving comprehensive environment coverage while reducing redundant computations and processing time.
Solution Approach 2:
The patent segments the multi-camera processing task into independent vertical column operations that can be performed in parallel. By dividing the comprehensive environment coverage task into separable vertical strip processing units, the system can process multiple camera inputs simultaneously with optimized resource utilization, reducing overall processing time while maintaining complete environmental awareness.
Data Source
AI summary
This disclosure provides systems, methods, and devices that support machine learning techniques for vehicle guidance. In one aspect, a method is provided that includes receiving an image frame from an image sensor, determining a projected image frame to correct a vertical axis of the image frame, and generating a top-down representation of the environment based on the projected image frame. The projected image frame may be aligned with the top down representation's vertical axis and may be projected using a cylindrical family projection. The top-down representation may be produced by assembling depth and context matrices from vertically aligned pixels in the projected image frame.