Virtual Asset Calibration Using Depth Range Limiting for Real-Time 3D Display
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Solution Overview
Problem
Existing three-dimensional modeling systems are processor-intensive, costly, and unsuitable for mobile or embedded devices, requiring significant manual labor and lacking real-time display capabilities.
Innovation Solution
A machine-implemented method for adjusting depth data recording by determining spatial ranges, tracking subjects, and calibrating recordings to compensate for lens distortion, allowing real-time display on devices with limited processing capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated modelling systems process images through image and feature recognition algorithms, then three-dimensional content can be generated automatically, but the systems exhibit processor intensive behaviours and are unsuitable for mobile or embedded processors
Solution Approach 1:
The patent extracts and removes unnecessary data points from the depth recording that do not contribute to the three-dimensional model, keeping only the essential information needed for rendering. This reduction in data volume makes the processing suitable for mobile and embedded devices while maintaining automated modelling capabilities.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the depth data. High-precision processing is applied only to areas containing the subject of interest, while peripheral areas receive reduced processing. This localized approach reduces overall computational complexity while maintaining model accuracy where needed.
2Manufacturing precision
If the entire volume of a given subject is recorded in depth data, then complete three-dimensional information is captured, but the data size becomes excessively large for mobile devices
Solution Approach 1:
The system extracts and removes redundant depth data points from the recording, keeping only those essential for reconstructing the three-dimensional model. This extraction process significantly reduces data size while preserving the completeness of the subject representation.
Solution Approach 2:
Instead of recording the entire volume with uniform high precision, the system applies partial action by recording only the necessary portions of the subject at high detail levels, while using lower detail levels for less critical areas, thereby reducing overall data size.
3Manufacturing precision
If traditional three-dimensional modelling methods are used, then realistic results can be achieved, but many hours of highly skilled labour are required
Solution Approach 1:
The system enables self-service automated modelling where the depth camera and processing algorithms work together to automatically generate three-dimensional content without requiring manual intervention or skilled labor. The automated processing maintains realistic results while dramatically reducing production time.
Solution Approach 2:
The patent replaces manual mechanical modelling processes with automated optical sensing and computational algorithms. The depth camera captures three-dimensional information optically, and software algorithms automatically process this data into realistic models, substituting skilled human labor with automated systems.
4Area of stationary object
If depth recording captures a wide spatial range, then more of the scene is recorded, but the data size and processing requirements increase
Solution Approach 1:
The system applies local quality by recording wide spatial ranges but using reduced precision and lower data density for distant or peripheral areas. High-precision recording is concentrated on the subject area, while surrounding spaces are captured with sufficient but lower detail, reducing overall processing requirements.
Data Source
AI summary
Embodiments generally relate to a machine-implemented method of automatically adjusting the range of a depth data recording executed by at least one processing device. The method comprises determining, by the at least one processing device, at least one positions of a subject to be recorded; determining, by the at least one processing device, at least one spatial range based on the positions of the subject; receiving depth information; and constructing, by the at least one processing device, a depth data recording based on the received depth information limited by the at least one spatial range.