Dynamic Multiscale Profilometric Imaging for Resolution and Power Balance
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Solution Overview
Problem
Existing profilometric imaging systems face challenges in achieving optimal resolution and power efficiency while adapting to varying distances and environmental conditions, leading to sub-optimal performance in capturing fine details and increased power consumption.
Innovation Solution
The implementation of multiscale pattern projection and detection systems that dynamically adjust camera, projector, and controller parameters to optimize profilometric imaging, allowing for adaptable resolution and efficient power usage across different imaging scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed-resolution profilometric imaging is used, then system simplicity is maintained, but adaptability to varying distances and environmental conditions deteriorates
Solution Approach 1:
The system dynamically adjusts the resolution of profilometric imaging based on detected environmental conditions and distance to the object of interest. The controller modifies camera and projector parameters in real-time, transitioning between different resolution modes to optimize performance for near-field and far-field imaging scenarios without requiring multiple fixed-resolution systems.
Solution Approach 2:
The system changes operational parameters including camera resolution, projector pattern scale, and focal length based on detected environmental conditions. By dynamically modifying these parameters, the system adapts to varying imaging scenarios while maintaining a single unified hardware platform, avoiding the complexity of multiple specialized systems.
2Measurement precision
If high resolution profilometric imaging is used, then fine detail capture is improved, but power consumption increases
Solution Approach 1:
The system dynamically switches between high-resolution and low-resolution imaging modes based on the imaging scenario. When fine detail capture is not required or the object is far away, the system operates in low-resolution mode to conserve power. When fine details need to be captured and conditions are favorable, it transitions to high-resolution mode, optimizing the balance between measurement precision and power consumption.
Solution Approach 2:
The controller adjusts camera resolution and projector pattern density parameters based on environmental conditions and imaging requirements. By dynamically modifying these parameters, the system achieves high measurement precision when needed while reducing power consumption during normal operation, eliminating the need to maintain constant high-power operation.
3Productivity
If dynamic parameter adjustment is implemented, then imaging performance is optimized, but device complexity increases
Solution Approach 1:
The system employs a controller that receives input from environmental sensors and object detection mechanisms, then automatically adjusts camera and projector parameters based on this feedback. This closed-loop control optimizes imaging performance for different scenarios without requiring manual intervention or complex user configuration, managing system complexity through automated decision-making algorithms.
Solution Approach 2:
The system performs self-configuration by automatically detecting imaging conditions and adjusting its own parameters without external control. The controller autonomously determines optimal resolution, focal length, and pattern scale based on environmental feedback, enabling the system to optimize its own performance while minimizing the complexity of external control interfaces.
4Adaptability or versatility
If multiscale pattern projection is used, then adaptability to different distances is improved, but pattern complexity increases
Solution Approach 1:
The projection system uses multiscale patterns that can be segmented into different scale levels. The controller selects and projects appropriate pattern scales based on the distance to the object of interest, using coarser patterns for far-field imaging and finer patterns for near-field imaging. This segmentation approach enables distance adaptability while managing pattern complexity through selective deployment.
Solution Approach 2:
The system employs nested multiscale patterns where multiple scales are integrated within a single projection framework. Different pattern scales are nested together, allowing the system to project the appropriate scale based on imaging conditions without requiring separate projection systems for different distances, thus managing complexity through hierarchical integration.
Data Source
AI summary
A system for dynamic profilometric imaging using multiscale pattern(s) is described. The system may include a projector that projects a multiscale pattern on an environment including a target of interest. The system may also include an imaging sensor that receives reflections of the projected multiscale pattern from the target of interest. The system may further include a controller of the camera and the projector which performs profilometric imaging of the target of interest by dynamically adjusting an operational parameter of the camera, the projector, and/or the controller. The multiscale pattern(s) may contain useful features at multiple scales and/or may be optimal at predetermined depths and/or resolutions. By dynamically adjusting the operational parameter, such a profilometric imaging system may perform there-dimensional (3D) scene reconstruction while maintaining optimal performance, power efficiency and profilometric resolution.