Neural Network 3D Surface Geometry Prediction
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Solution Overview
Problem
Current computer vision techniques for predicting object geometry, such as 3D morphable models, are resource-intensive and sensitive to deviations in object contours, making them inefficient for tasks like facial recognition on mobile devices without depth sensors.
Innovation Solution
Training a neural network model to predict mesh vertices representing the surface geometry of objects, using iterative refinement with human annotator adjustments and a lightweight architecture that accommodates expressivity and deformations, such as a face with a closed eye, without requiring depth sensor information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D morphable models are used to estimate pose and scale, then object geometry prediction accuracy is improved, but computational resource consumption increases and sensitivity to contour deviations worsens
Solution Approach 1:
The patent segments the object geometry representation into a hierarchical structure: a coarse 3D morphable model provides overall pose and scale estimation, while a refined mesh model with densely spaced vertices captures detailed surface geometry. This segmentation allows the system to benefit from both the robustness of the 3DMM and the precision of detailed mesh representation without requiring full computational complexity of traditional 3DMM approaches throughout the entire processing pipeline.
Solution Approach 2:
The patent transitions from traditional 3D spatial representation to 2D image space by projecting the 3D mesh model onto the image plane. This dimensionality change allows the system to leverage 2D image data from standard cameras (without depth sensors) to infer 3D geometry, reducing computational requirements while maintaining accuracy. The projected mesh vertices are optimized in 2D space to match detected object contours, enabling efficient geometry estimation on mobile devices.
2Measurement precision
If 3D morphable models are used for object geometry prediction, then accuracy is improved, but sensitivity to contour deviations increases
Solution Approach 1:
The patent employs a two-stage optimization process that cushions against contour deviation sensitivity. First, the 3D morphable model provides a robust initial estimate of pose and scale that is relatively insensitive to contour variations. Second, the mesh refinement stage optimizes vertex positions to match the actual detected contours, compensating for any deviations. This staged approach cushions the system against the harmful effects of contour deviations while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediate projected mesh model that serves as a mediator between the 3D morphable model and the final geometry prediction. This intermediate representation is optimized to match detected contours in 2D image space, acting as a buffer that reduces the direct impact of contour deviations on the final 3D geometry estimation. The intermediary layer allows the system to adapt to contour variations without propagating errors to the final result.
3Adaptability or versatility
If detailed mesh models are used to accommodate object deformations, then expressivity is improved, but model complexity increases
Solution Approach 1:
The patent implements a dynamic mesh refinement process where the level of mesh detail is adaptively adjusted based on the specific object and deformation requirements. The mesh model starts with a base structure from the 3D morphable model and dynamically adds refinement levels only where needed to capture specific deformations. This dynamic approach maintains high expressivity for accommodating various object deformations while keeping the average model complexity manageable for mobile device deployment.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network model to predict mesh vertices corresponding to a three-dimensional surface geometry of an object depicted in an image.