Camera System Depth Estimation Using Directional Optics and Machine Learning
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Solution Overview
Problem
Pseudo-lidar systems that rely on multiple cameras and sensors face computational complexity and inefficiency in accurately estimating depth, leading to increased processing tasks and delays in detecting objects in a scene.
Innovation Solution
A camera system utilizing directional optics and a machine learning model to estimate depth, which reduces computation by processing image data from a lens and detector, and uses inverted or graded lenses with per pixel or quadrant filtering to resolve angles of lightwaves, improving image detection and simplifying subsequent machine learning tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pseudo-lidar systems use multiple cameras and sensors to accurately estimate depth, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detector is divided into multiple sections, with each section having associated filtering optics that resolve specific angles of incoming lightwaves. This segmentation allows the system to extract depth information from a single camera by spatially separating light from different directions, achieving pseudo-lidar functionality without requiring multiple cameras.
Solution Approach 2:
The patent adds an angular dimension to the image capture process by resolving angles of lightwaves in addition to spatial position. This is achieved through filtering optics that direct light at different angles to different detector sections, enabling depth estimation by adding angular information to the traditional 2D image plane.
2Measurement precision
If pseudo-lidar systems combine data from multiple sensors to produce spatial point distribution, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The filtering optics perform preliminary action by resolving angles of lightwaves before detection. The optical system pre-processes the light by directing it to appropriate detector sections based on angle, so that the computational task is simplified to reading pre-organized data rather than performing complex post-processing of raw multi-sensor data.
Solution Approach 2:
The patent replaces computational processing with optical processing. Instead of using multiple sensors and computationally intensive algorithms to estimate depth, the system uses optical elements (filtering optics) to physically separate and direct light based on angle, with the detector simply recording the spatial distribution of resolved light.
3Measurement precision
If pseudo-lidar systems use multiple cameras for depth estimation, then measurement precision is improved, but loss of time increases due to time-consuming image overlap search
Solution Approach 1:
The filtering optics perform preliminary action by resolving angles of lightwaves before detection. The optical system pre-processes the light by directing it to appropriate detector sections based on angle, so that the computational task is simplified to reading pre-organized data rather than performing complex post-processing of raw multi-sensor data.
4Measurement precision
If pseudo-lidar systems use multiple sensors and cameras, then measurement precision is improved, but weight increases
Solution Approach 1:
The patent merges the functions of multiple sensors into a single integrated system. The filtering optics are integrated with a single detector array, combining angle-resolving optics, wavelength filtering, and detection in one compact unit, eliminating the need for multiple separate cameras and sensors while maintaining depth estimation capability.
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 system achieves efficient and accurate depth estimation with reduced processing complexity and hardware size, generating improved image data that simplifies subsequent machine learning tasks, similar to LIDAR systems but with simpler hardware.
Implementation Method 1
a detector that uses a lens to resolve multiple angles of light per section of the detector
Implementation Method 2
process the image data using the ML model to produce the depth according to the size of the kernel
Data Source
AI summary
System, methods, and other embodiments described herein relate to estimating depth using a machine learning (ML) model. In one embodiment, a method includes acquiring image data according to criteria from a detector that uses a lens to resolve multiple angles of light per section of the detector. The method also includes mapping a kernel to the image data according to a view associated with the section and a size of the kernel. The method also includes processing the image data using the ML model to produce the depth according to the size of the kernel.