Single Smartphone Human Body 3D Reconstruction Using AR Guidance
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Solution Overview
Problem
Current methods for human body reconstruction, either using professional multi-camera systems or single portable devices like smartphones, face limitations such as high costs, large space requirements, and inability to handle texture-less areas and small movements, resulting in low-integrity and low-precision reconstruction results.
Innovation Solution
A method and system for human body three-dimensional modeling using a single mobile phone, incorporating augmented reality for data acquisition and a deformable implicit neural radiance field for reconstruction, which includes fitting a parametric template mesh, guiding data acquisition, and optimizing implicit spatial deformation, signed distance, and color estimation models through volume rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a professional multi-camera acquisition system is used for human body scanning, then reconstruction precision and integrity are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent divides the human body into multiple mesh regions (head, torso, limbs, etc.) and processes each region separately through region-specific rendering. This segmentation allows the system to focus computational resources on capturing detailed features of each body part independently, achieving high reconstruction precision without requiring a complex multi-camera system to capture all details simultaneously.
Solution Approach 2:
The patent introduces an intermediary processing system that uses a single camera to capture images, which are then processed through mesh fitting, region division, and selective rendering algorithms. This intermediary computational pipeline acts as a mediator between the simple single-camera input and the high-precision 3D reconstruction output, eliminating the need for complex multi-camera hardware while maintaining reconstruction quality.
2Measurement precision
If a professional multi-camera acquisition system is used for human body scanning, then reconstruction precision is improved, but space requirements increase
Solution Approach 1:
The patent transitions from spatial arrangement of multiple cameras in physical space to computational processing in digital space. By capturing images with a single camera and using mesh-based rendering to synthesize views from different angles, the system eliminates the need for multiple cameras arranged in a large physical space, achieving high precision reconstruction without increasing physical footprint.
3Device complexity
If a single portable device is used for image acquisition, then device complexity is reduced, but ability to handle texture-less parts and small movements deteriorates
Solution Approach 1:
The patent applies different processing strategies to different regions of the human body mesh. For texture-less areas and regions with small movements, the system uses region-specific rendering that incorporates contextual information from surrounding areas and temporal information from multiple frames. This local quality approach ensures that difficult-to-capture regions receive specialized processing attention, maintaining reconstruction integrity despite using a simple single-camera device.
Solution Approach 2:
The patent performs preliminary mesh fitting and region classification before the actual image capture and rendering process. By pre-establishing the mesh structure and identifying critical regions that require special attention, the system can prioritize computational resources during image processing to ensure accurate capture of texture-less parts and small movements, compensating for the limitations of a single portable camera.
4Device complexity
If traditional multi-view stereo reconstruction method is used with a single portable device, then device complexity is reduced, but reconstruction time increases due to insufficient data quality
Solution Approach 1:
The patent implements a dynamic rendering process that adapts to the captured images in real-time. The system dynamically adjusts rendering parameters, selects optimal view angles, and prioritizes processing of critical regions based on the actual captured data quality. This dynamic approach allows the system to efficiently process images from a single portable device, maintaining high reconstruction quality while minimizing processing time by focusing computational effort where most needed.
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
Enables high-quality, high-precision human body reconstruction efficiently using a single smartphone by guiding data acquisition with augmented reality and leveraging deformable implicit neural radiance fields to improve reconstruction accuracy and integrity.
Implementation Method 1
optimizing an implicit spatial deformation field estimation model, an implicit signed distance field estimation model and an implicit color estimation model by means of volume rendering to obtain a three-dimensional human body model
Data Source
AI summary
A data acquisition and reconstruction method and a data acquisition and reconstruction system for human body three-dimensional modeling based on a single mobile phone. In the aspect of data acquisition, the present application only uses a single smart phone, and uses augmented reality technology to guide users to collect high-quality video data input for a reconstruction algorithm, so as to ensure that the subsequent human body reconstruction algorithm can stably obtain a high-quality three-dimensional human body model. In the aspect of a reconstruction algorithm, the present application designs a deformable implicit neural radiance field. The use of an implicit spatial deformation field estimation model solves the problem that the subject has small motion in the process of collecting data with a single mobile phone; the implicit signed distance field is used to represent human geometry, which has rich expressive ability and improves the accuracy of three-dimensional human model reconstruction.