Composite 3D Blob Imaging for Shadowed Object Measurement
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Solution Overview
Problem
3D camera systems struggle to capture accurate image data of complex objects due to shadows and occlusions, particularly when objects are arranged in groups, leading to loss of data between items.
Innovation Solution
A composite 3D blob tool that analyzes both present and absent image data, using calibration information to estimate and remove shadows, allowing for accurate computation of x, y, and z dimensions by connecting segments of image data to areas of missing data and generating a bounding box.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D camera systems are used to capture objects in groups, then measurement capability is provided, but image data is lost between objects due to shadows and occlusions
Solution Approach 1:
The patent converts the harmful effect of shadows and occlusions into beneficial information by treating absent image data as meaningful input. The composite 3D blob tool analyzes both present and absent data patterns to infer object boundaries, transforming data loss into a useful signal for complete object reconstruction including areas beneath and between objects.
Solution Approach 2:
The patent introduces calibration information as an intermediary element that bridges the gap between incomplete image data and accurate 3D measurements. By incorporating calibration data about the camera system and lighting conditions, the tool can estimate and compensate for shadow effects, enabling precise measurement despite data loss.
2Adaptability or versatility
If traditional 3D imaging methods are used, then simple objects can be measured, but complex objects with varying heights and shadows cannot be accurately imaged
Solution Approach 1:
The patent applies dynamics by making the imaging method adaptable to varying object configurations. The composite 3D blob tool dynamically adjusts its analysis based on detected height variations, shadow patterns, and object arrangements. It can handle both simple and complex objects by flexibly interpreting present and absent data in different scenarios, maintaining precision across diverse object types.
Solution Approach 2:
The patent moves from traditional 2D image analysis to 3D spatial reasoning by incorporating z-height information and shadow geometry. The tool uses the third dimension to disambiguate overlapping objects, estimate occluded areas, and reconstruct complete object shapes even when parts are hidden, thereby handling complex multi-level object arrangements effectively.
3Area of stationary object
If image data is captured from above, then top surfaces are visible, but areas between and beneath objects are lost to shadows
Solution Approach 1:
The patent performs preliminary analysis of shadow patterns and absent data regions before final object reconstruction. By pre-identifying shadowed areas and using calibration information to estimate their characteristics, the tool prepares compensation strategies in advance, allowing complete object reconstruction that includes normally hidden areas beneath and between objects.
Solution Approach 2:
The patent creates a complete 3D model copy of objects by inferring missing information from available data patterns. The composite blob tool generates virtual representations of occluded areas based on object geometry, shadow constraints, and calibration data, producing a full digital twin even when physical image capture is incomplete due to shadows.
Data Source
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AI summary
This invention provides a system and method that performs 3D imaging of a complex object, where image data is likely lost. Available 3D image data, in combination with an absence/loss of image data, allows computation of x, y and z dimensions. Absence/loss of data is assumed to be just another type of image data, and represents the presence of something that has prevented accurate data from being generated in the subject image. Segments of data can be connected to areas of absent data and generate a maximum bounding box. The shadow that this object generates can be represented as negative or missing data, but is not representative of the physical object. The height from the positive data, the object shadow size based on that height, the location in the FOV, and the ray angles that generate the images, are estimated and the object shadow size is removed from the result.