3D Body Scanning With Reused Transforms for Faster Color Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D scanning technologies face challenges in efficiently generating high-resolution, high-accuracy 3D color models of users, particularly in scenarios where computational resources are limited, and require extensive bandwidth for data transfer.
Innovation Solution
A method involving a scanning system that records depth and color images during an initial scan cycle, leveraging remote computing resources for high-accuracy model generation, followed by subsequent scan cycles that utilize locally computed transforms to generate similar models with reduced computational and bandwidth demands, using a combination of depth and color patch definitions from the initial cycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution 3D color models are generated using traditional scanning methods, then model accuracy is improved, but computational resources and bandwidth requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by capturing depth images and computing transforms during an initial scan cycle, storing these transforms for reuse in subsequent scan cycles. This preliminary computation of transformation matrices and alignment data enables faster processing in later cycles without sacrificing model accuracy, directly reducing computational resources required during repeated scanning operations
Solution Approach 2:
The system creates a simplified representation by copying and reusing transforms from the initial scan cycle for subsequent scan cycles. Instead of recomputing all transforms from scratch, the system copies the established transformation relationships and applies them to new depth images, maintaining model accuracy while significantly reducing computational overhead and energy consumption
2Measurement precision
If high-resolution 3D color models are generated using traditional scanning methods, then model accuracy is improved, but bandwidth requirements increase significantly
Solution Approach 1:
The system extracts and reuses only the essential transformation data from the initial scan cycle, separating the computationally intensive transform computation from the actual model generation process. By extracting transforms and storing them independently, the system reduces bandwidth requirements for subsequent scans while maintaining the ability to generate high-resolution color models through efficient local processing
Solution Approach 2:
The system performs preliminary data preparation by computing and storing transforms during the first scan cycle before subsequent scanning operations. This preliminary action reduces the data transfer requirements for later cycles, as only minimal scan parameters need to be transmitted while the heavy computational work of transform generation has already been completed and stored locally
3Measurement precision
If complete scan cycles are performed for each 3D model generation, then model quality is maintained, but processing time increases
Solution Approach 1:
The system implements periodic action by alternating between comprehensive initial scan cycles and abbreviated subsequent scan cycles. The initial cycle performs complete processing to establish transforms, while subsequent cycles leverage these transforms for faster model generation. This periodic pattern maintains model quality through the thorough initial scan while reducing processing time in repeated operations
Solution Approach 2:
The system performs preliminary comprehensive processing in the initial scan cycle to establish accurate transforms and alignment relationships. This preliminary action ensures model quality is maintained while enabling subsequent abbreviated cycles to operate faster, as the foundational transformation data has already been computed and stored for reuse
Data Source
AI summary
One variation of a method for modeling a human body includes: driving a sensor block along a path above a platform occupied by a user and recording a sequence of depth images and color images, via the sensor block, at each capture position in a sequence of capture positions along the path; compiling the set of depth images into a low-density 3D model of the user and a high-density 3D model of the user; extracting a set of color patches, from the sequence of color images, corresponding to discrete regions on a surface of the low-density 3D model of the user; projecting the set of color patches onto the surface of the low-density 3D model to generate a 3D color model of the user; and extracting a value of a dimension, projected from the low-density 3D color model onto the high-density 3D model, from the high-density 3D model.