LiDAR-Assisted Image Sensor Settings Adjustment
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Solution Overview
Problem
Existing image capture technologies often result in suboptimal photographs due to inadequate initial settings such as angle, brightness, and exposure, which cannot be adequately corrected by editing tools after capture.
Innovation Solution
The use of Light Detection and Ranging (LiDAR) data in conjunction with an AI model to determine and adjust image sensor settings and frame of reference prior to capturing an image, incorporating location, elevation, and light energy intensity data to recommend optimal camera settings and positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image capture is performed with standard settings, then the capture process is simple and fast, but the image quality is suboptimal due to inadequate settings
Solution Approach 1:
The system performs preliminary analysis of the capture scene using LiDAR depth data and environmental sensors before the image capture occurs. This advance analysis determines optimal camera settings (exposure, focus, brightness) and frame of reference (composition guidelines) in advance, so that when capture happens, the settings are already optimized without requiring complex real-time adjustments or post-processing.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives raw scene data from multiple sensors (LiDAR, environmental sensors), analyzes this data to determine optimal settings, and then applies these settings to the image capture process. This intermediary layer acts as a mediator between the raw sensor data and the final image output, automatically optimizing quality without requiring direct user intervention in the complex settings adjustment process.
2Manufacturing precision
If post-capture editing is used to correct suboptimal images, then the initial capture can be simple, but significant quality improvements are limited
Solution Approach 1:
The system performs all necessary optimization actions before the image capture occurs. By analyzing the scene in advance using LiDAR and environmental data, the system determines and applies the optimal exposure, focus, brightness, and composition settings prior to capture. This preliminary optimization eliminates or minimizes the need for time-consuming post-capture editing, as the image is already optimized at the moment of capture.
3Manufacturing precision
If multiple sensors are integrated to determine optimal settings, then image quality improves, but the device complexity increases
Solution Approach 1:
The patent integrates multiple sensors (LiDAR, environmental sensors, camera) into a unified system where each sensor serves multiple functions. The LiDAR sensor not only provides depth information but also helps determine spatial composition and subject-distance relationships. Environmental sensors provide both lighting conditions and atmospheric context. This multi-functionality approach allows the system to achieve high image quality through comprehensive scene analysis without proportionally increasing device complexity, as the same sensors serve multiple analytical purposes simultaneously.
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
This approach enables the capture of higher-quality images by adjusting settings like focus, brightness, and exposure before taking the photo, aligning the live image with a suggested frame of reference for improved composition and lighting, thereby enhancing the quality of the final image.
Implementation Method 1
receiving, at a processing resource of a computing device via a LiDAR sensor, first signaling indicative of at least one of location data, elevation data, or light energy intensity data
Data Source
AI summary
Methods and devices related to determining image sensor settings using LiDAR are described. In an example, a method can include receiving, at a processing resource via a LiDAR sensor, first signaling indicative of location data, elevation data, and/or light energy intensity data associated with an object, receiving, at the processing resource via an image sensor, second signaling indicative of data representing an image of the object, generating, based at least in part on the first signaling, additional data representing a frame of reference for the object, transmitting to a user interface third signaling indicative of the data representing the frame of reference for the object and the data representing the image of the object, and displaying, at the user interface and based at least in part on the third signaling, another image that comprises a combination of the frame of reference and the data representing the image.