Mobile Depth Capture Feedback for Accurate Object Dimensioning
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Solution Overview
Problem
Depth sensors in mobile devices often incompletely capture object surfaces and include artifacts due to multipath reflections, leading to inaccurate dimensioning of objects.
Innovation Solution
A computing device equipped with a depth sensor and processor that captures a point cloud and two-dimensional image, assesses the object's position and conditions, and provides feedback to improve positioning and material compatibility for accurate dimensioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a depth sensor captures a point cloud to determine object dimensions, then dimensioning capability is enabled, but measurement precision deteriorates due to incomplete surface capture and multipath reflection artifacts
Solution Approach 1:
The system provides real-time feedback to the user about the quality of the point cloud capture, including indicators for surface coverage completeness and detection of multipath reflection artifacts. This feedback enables the user to adjust the capture position or angle to improve measurement precision while maintaining reliable data collection.
Solution Approach 2:
Before final dimensioning is performed, the system executes preliminary capture quality assessment to identify incomplete surfaces or artifact-contaminated regions. This preliminary action allows for early detection of problematic captures and enables corrective actions before inaccurate measurements are generated.
2Measurement precision
If multiple captures are performed to improve dimensioning accuracy, then measurement precision improves, but productivity decreases due to increased time and operational complexity
Solution Approach 1:
The system provides immediate feedback after each capture indicating whether the point cloud quality is sufficient for accurate dimensioning. This feedback mechanism enables the operator to quickly determine if another capture is needed, reducing unnecessary repeated captures and improving overall productivity while maintaining measurement precision.
Solution Approach 2:
The system automatically assesses capture quality and provides guidance for improvement without requiring external expertise or multiple manual review cycles. The self-service quality assessment enables operators to independently make decisions about whether to recapture, streamlining the workflow and improving productivity.
3Measurement precision
If the object position is not optimized within the field of view, then ease of operation is maintained, but measurement precision deteriorates due to incomplete object capture
Solution Approach 1:
The system provides real-time feedback to the user about the object's position within the field of view and the resulting capture quality. This feedback includes guidance on optimal positioning to ensure complete surface capture, making the positioning requirement intuitive and easy to follow while maintaining high measurement precision.
Solution Approach 2:
The system uses visual indicators (such as color-coded feedback) to communicate capture quality and positioning status to the user. Different colors indicate different levels of capture completeness or artifact presence, providing an intuitive visual guide for optimizing object position without requiring complex technical knowledge.
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
Enhances the accuracy and efficiency of object dimensioning by ensuring complete capture and addressing positional and material-related issues, reducing the need for multiple captures and improving dimensioning performance.
Implementation Method 1
depth sensors such as time-of-flight (ToF) sensors can be deployed in mobile devices such as handheld computers, and employed to capture point clouds of objects
Implementation Method 2
depth sensors such as time-of-flight (ToF) sensors can be deployed in mobile devices such as handheld computers
Data Source
AI summary
A method in a computing device includes: capturing, via a depth sensor having a field of view, (i) a point cloud depicting an object resting on a support surface, and (ii) a two-dimensional image depicting the object and the support surface; based on the point cloud, detecting a portion of the object; determining, based on the portion of the object, whether a position of the object within the field of view meets a positional criterion; when the position of the object within the field of view does not meet the positional criterion, generating a positional feedback instruction; and controlling a display to present the positional feedback instruction.