Graded Lens Camera System for Depth Estimation
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Solution Overview
Problem
Pseudo-LIDAR systems using multiple cameras and sensors are computationally intensive and face challenges in efficiently and accurately estimating depth due to image distortion and processing complexity, particularly when dealing with moving vehicles and objects.
Innovation Solution
A camera system employing directional optics and a graded lens to resolve lightwave angles, integrating multiple views into a single image using a detector array, which simplifies depth estimation through per pixel or quadrant filtering, reducing computational load and image distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras and sensors are used to improve depth estimation accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple camera views into a single integrated depth estimation process using a unified neural network model. Instead of processing images from multiple cameras separately and then merging results, the system processes all views simultaneously through a single model that directly outputs depth information, reducing system complexity while maintaining accuracy.
Solution Approach 2:
The neural network model is designed to handle multiple functions: it processes images from different camera views, performs depth estimation, and generates structured output all within a single unified framework. This multi-functional approach eliminates the need for separate processing pipelines for each camera, simplifying the overall system architecture.
2Measurement precision
If multiple cameras and sensors are used to improve depth estimation accuracy, then measurement precision is improved, but computational load increases
Solution Approach 1:
The patent merges the processing of multiple camera inputs into a single computational pass through the neural network. By feeding all camera views simultaneously into one model rather than processing them sequentially or separately, the system reduces the total computational operations required while achieving the same depth estimation accuracy.
3Measurement precision
If image overlap search is performed to resolve images from multiple cameras, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary alignment of camera views by incorporating spatial transformation layers within the neural network that pre-adjust for camera positions and orientations. This preliminary action eliminates the need for time-consuming post-capture image overlap search and matching, as the network is already prepared to process aligned inputs directly.
4Device complexity
If a single camera with directional optics is used to reduce device complexity, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent transitions from spatial dimension (multiple physical cameras) to functional dimension (virtual multiple views through neural network processing). By using a single camera that captures images processed through different virtual viewpoints generated by the network, the system achieves multi-view depth estimation without the physical complexity of multiple cameras.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The camera system effectively estimates depth with reduced computational complexity, producing feature-rich image data that can be parsed efficiently, similar to LIDAR systems, improving object detection accuracy and processing speed.
Implementation Method 1
The camera system includes a graded lens to receive light associated with a scene and resolve multiple angles of the light according to parameters of the graded lens
Implementation Method 2
The camera system also includes a detector array that senses the light from the graded lens per pixel to integrate multiple views of the scene into a single image
Data Source
AI summary
System, methods, and other embodiments described herein relate to an improved camera system including directional optics to estimate the depth of grayscale and color images. In one embodiment, a camera system includes a graded lens to receive light associated with a scene and resolve multiple angles of the light according to parameters of the graded lens. The camera system also includes a detector that senses the light from the graded lens per pixel to integrate multiple views of the scene into a single image to estimate depth associated with objects and the single image includes data for views of the objects that overlap having resolved angles in association with the parameters.