Lensless Compressive Camera 3D Reconstruction via Sensor Segmentation
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Solution Overview
Problem
Current image reconstruction methods face challenges in efficiently reconstructing three-dimensional images using lensless compressive image acquisition, which involves collecting and processing large amounts of raw data effectively.
Innovation Solution
A processor-based system with a programmable aperture and a pair of sensors is used to determine depth information from camera geometry and disparity maps, enabling the reconstruction of three-dimensional images by processing compressive measurements from a lensless compressive camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If lensless compressive image acquisition is used to reduce data requirements and cost, then device complexity and power consumption are reduced, but measurement precision and image reconstruction quality may deteriorate
Solution Approach 1:
The camera system is segmented into two separate sensors instead of using a single complex lens-based camera. Each sensor captures compressive measurements from different perspectives, and the processor segments the reconstruction process to generate multiple 2D images that are combined into a 3D representation. This segmentation reduces device complexity while maintaining measurement precision through computational synthesis.
Solution Approach 2:
A processor acts as an intermediary between the simple lensless sensors and the final 3D image output. The processor performs computational reconstruction, combining compressive measurements from multiple sensors and applying algorithms to generate high-quality 3D images. This intermediary computational step bridges the gap between simple hardware and complex output requirements.
2Measurement precision
If multiple cameras are used to capture 3D images, then measurement precision and depth information are improved, but device complexity and cost increase
Solution Approach 1:
Multiple simple sensors perform multiple functions: each sensor captures compressive measurements that contribute to both 2D image reconstruction and 3D depth information extraction. The system achieves multi-functionality where the same hardware components serve both imaging and depth mapping purposes, eliminating the need for separate depth sensors or multiple complex cameras.
Solution Approach 2:
The system merges the functions of multiple cameras into a unified computational framework. Compressive measurements from multiple sensors are combined through processing algorithms that synthesize both 2D images and 3D depth information simultaneously. This merging approach achieves multi-camera functionality with simpler individual components.
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 allows for efficient three-dimensional image reconstruction with reduced data requirements, enabling low-power and low-cost 3D image capture, suitable for applications like Internet-of-Things devices, without the need for multiple cameras.
Implementation Method 1
a pair of sensors configured to detect light passing through the programmable aperture
Data Source
AI summary
The present disclosure generally discloses a three-dimensional (3D) image reconstruction capability. The 3D image reconstruction capability may be configured to support reconstruction of a 3D image of a scene. The 3D image reconstruction capability may be configured to support reconstruction of a 3D image of a scene based on lensless compressive image acquisition performed using a lensless compressive camera having a single aperture and a set of multiple sensors. The reconstructed 3D image of a scene may include (1) image data indicative of a set of multiple two-dimensional (2D) images reconstructed based on the set of multiple sensors of the lensless compressive camera (which may be represented as images) and (2) depth information indicative of depths at points or areas of an overlapping portion of the multiple images reconstructed based on the set of multiple sensors of the lensless compressive camera (which may be represented as a depth map).