Dynamic Field of View for Multi-Camera Bird's Eye View Perception
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Solution Overview
Problem
Existing multi-camera systems struggle to effectively process image data from overlapping fields of view, leading to distorted object representations and reduced accuracy in tasks like object detection and depth estimation.
Innovation Solution
The system employs a dynamic field of view unit and stochastic depth scaling to process camera images, focusing on relevant objects and adapting to their size and location within the scene, thereby generating improved bird's eye view features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed field of view is used in multi-camera systems, then the system structure is simple, but object distortion increases when objects are displaced from the optical axis or far from the lens
Solution Approach 1:
The patent implements a dynamic field of view mechanism that adjusts the FOV of each camera based on the spatial location and importance of detected objects. Instead of using fixed FOV parameters, the system dynamically modifies the FOV to optimize object representation, reducing distortion for objects far from the optical axis while maintaining system feasibility through automated adjustment.
Solution Approach 2:
The system changes the FOV parameter dynamically based on object characteristics. By adjusting the FOV parameter according to object distance and position relative to the optical axis, the system optimizes image quality and reduces distortion without requiring complete system redesign.
2Device complexity
If all camera images are processed with equal importance, then the processing is simple, but important objects may be missed due to background distraction
Solution Approach 1:
The patent applies local quality by assigning different processing priorities to different regions of the image based on object importance. The system identifies key objects and allocates more processing resources to regions containing these objects, while reducing processing for background areas. This ensures important objects are detected with high accuracy without uniformly increasing processing complexity across the entire image.
Solution Approach 2:
The system performs partial processing by focusing computational resources on salient objects rather than processing all image regions with equal depth. The dynamic FOV mechanism selectively enhances processing for regions containing important objects, applying excessive action only where needed rather than uniformly across the entire scene.
3Area of stationary object
If the field of view is expanded to capture more scene, then more objects are visible, but object distortion increases for objects far from the lens
Solution Approach 1:
The system dynamically adjusts the FOV for each camera based on the distance and position of detected objects. For objects far from the lens, the FOV is adjusted to provide a more magnified view, reducing distortion. For closer objects, the standard FOV is maintained. This dynamic adjustment allows the system to maintain both scene coverage and object accuracy by adapting FOV parameters in real-time.
Data Source
AI summary
An apparatus for processing image data includes a memory for storing the image data, wherein the image data comprises a first set of image data collected by a first camera comprising a first field of view (FOV) and a second set of image data collected by a second camera comprising a second FOV; and processing circuitry in communication with the memory. The processing circuitry is configured to: apply an encoder to extract, from the first set of image data, a first set of perspective view features; apply the encoder to extract, from the second set of image data, a second set of perspective view features; and project the first set of perspective view features and the second set of perspective view features onto a grid to generate a set of bird's eye view (BEV) features.