3D Face Model Fitting Using Depth Map and 2D Image
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Solution Overview
Problem
Current methods for facial three-dimensional modeling are inefficient and lack precision, making them unsuitable for widespread use beyond large organizations due to high costs and limited accessibility.
Innovation Solution
A method and device that utilize a three-dimensional face database to fit a three-dimensional face model by receiving a two-dimensional image and its corresponding depth map, estimating an initial transformation matrix using shape and expression feature vectors, and iteratively refining the model to minimize distance errors between projected feature points and depth map points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professional sensors such as laser sensor, structured light sensor and large-scale camera arrays sensor are used, then the accuracy of reconstructing three-dimensional models is high, but the cost is high and it is only suitable for large organization
Solution Approach 1:
The patent uses a two-dimensional image as a copy or projection of the three-dimensional object, combined with depth map information, to reconstruct the full 3D model. This avoids the need for expensive professional 3D sensors by using readily available 2D imaging technology supplemented with depth information.
Solution Approach 2:
The patent introduces a depth map as an intermediary element that bridges the gap between simple 2D imaging and full 3D reconstruction. The depth map provides additional spatial information that enables accurate 3D modeling without requiring complex professional sensors.
2Device complexity
If the device with a depth camera that can automatically move is used, then low-cost three-dimensional image capture is achieved, but the user must rotate according to voice prompt and the process is time-consuming
Solution Approach 1:
The patent achieves complete 3D reconstruction using only a single 2D image and its corresponding depth map, rather than requiring multiple images captured from different angles. This partial action (using minimal input data) is sufficient to produce the complete 3D model, dramatically reducing the time required.
Solution Approach 2:
The system performs automatic 3D reconstruction without requiring user intervention for rotation or multiple captures. The algorithm automatically processes the single 2D image and depth map to generate the complete 3D model, eliminating the need for users to follow voice prompts or manually rotate the device.
3Ease of operation
If the method for facial three-dimensional modeling in the prior art is used, then the process is simple, but the efficiency and accuracy are low
Solution Approach 1:
The patent improves modeling efficiency and accuracy by optimizing key parameters including the use of feature point detection, transformation matrix estimation, and iterative fitting algorithms. These parameter optimizations enable faster convergence to accurate 3D models while maintaining operational simplicity.
Solution Approach 2:
The patent employs an iterative fitting process that uses feedback from the comparison between projected feature points and actual depth map points to continuously refine the 3D model. This feedback mechanism improves both accuracy and efficiency by systematically reducing errors through multiple refinement iterations.
Data Source
AI summary
The present invention provides a method and a device for three-dimensional modeling, used in the field of image processing. The method includes: receiving a first two-dimensional image and a depth map corresponding to the first two-dimensional image; the first two-dimensional image and the depth map respectively include a face; fitting three-dimensional face model by a first three-dimensional face database according to a position of a face feature point in the first two-dimensional image and the depth map corresponding to the first two-dimensional image. The method and the device for three-dimensional modeling provided by the present invention can improve the efficiency of the facial three-dimensional modeling and the precision of modeling.