Quad-Dominant Mesh Extraction via Learned Orientation and Position Fields
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Solution Overview
Problem
Existing methods for generating 3D models from images often produce dense or irregular triangle-based meshes that are difficult to manipulate and lack detailed control, making them unsuitable for production pipelines.
Innovation Solution
A technique that uses machine learning models to generate a quad-dominant mesh from input images by learning orientation and position fields associated with the triangle mesh, allowing for the extraction of a quad-dominant mesh with explicit control over topology and material properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If triangle-based mesh extraction methods are used, then high-fidelity object details can be captured, but the resulting meshes have irregular topology that is difficult to manipulate and incompatible with quad-based subdivision techniques
Solution Approach 1:
The patent transforms the mesh representation by changing the topological parameter from triangle-based to quad-based structure. This is achieved through learning orientation and position fields that guide the extraction of quad-dominant meshes, making the mesh compatible with quad-based subdivision techniques while preserving object details.
Solution Approach 2:
The patent replaces traditional mechanical mesh conversion processes with a machine learning-based approach. Instead of using fixed algorithms to convert triangle meshes to quads, the system learns optimal orientation and position fields through neural networks, enabling end-to-end differentiable optimization that produces directly usable quad-dominant meshes.
2Ease of operation
If field-aligned quad remeshing techniques are used, then quad-dominant meshes can be generated, but the methods are non-differentiable and prevent optimization in end-to-end frameworks
Solution Approach 1:
The patent substitutes non-differentiable field-aligned remeshing algorithms with differentiable machine learning models. The orientation and position fields are learned through neural networks that support gradient computation, enabling the entire pipeline from image input to mesh output to be optimized end-to-end using gradient descent.
Solution Approach 2:
The patent changes the computational approach from discrete algebraic operations in traditional remeshing to continuous differentiable operations in neural networks. This allows the orientation and position fields to be optimized through backpropagation, integrating mesh generation into the broader optimization framework.
3Manufacturing precision
If traditional production pipelines are used, then manual refinement can be performed, but extensive artist time and effort are consumed
Solution Approach 1:
The patent enables the system to automatically generate high-quality quad-dominant meshes without requiring manual artist intervention. The machine learning models self-optimize the mesh extraction process by learning from input images and producing directly usable outputs that are compatible with production pipelines, eliminating the need for time-consuming manual refinement.
Solution Approach 2:
The patent extracts the essential geometric and topological information directly from images using learned orientation and position fields. By taking out only the necessary information needed for quad-mesh generation and optimizing this extraction process through end-to-end learning, the system produces production-ready meshes without requiring subsequent manual processing.
4Measurement precision
If triangle meshes with excessive geometry are extracted, then high-fidelity views can be generated, but the meshes are incompatible with quad-based subdivision techniques used in digital content creation
Solution Approach 1:
The patent changes the topological parameter of the extracted mesh from triangle-based to quad-based structure. By learning orientation fields that guide quad formation and position fields that control vertex placement, the system produces meshes with topology specifically suited for quad-based subdivision techniques while maintaining high view fidelity.
Solution Approach 2:
The patent segments the mesh generation process into distinct learnable components: orientation field learning for directional information, position field learning for vertex placement, and quad extraction for topology formation. This segmentation allows each component to be optimized independently while ensuring overall compatibility with production pipelines.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for generating a quad-dominant mesh of an object. The technique includes generating, via at least one of a first set of machine learning models, a three-dimensional (3D) triangle mesh of an object based on one or more two-dimensional input images of the object, iteratively learning, via a second set of machine learning models, an orientation field and a position field associated with a set of vertices included in the 3D triangle mesh, extracting a quad-dominant mesh associated with the object from the input triangle mesh based on the orientation field and the position field, wherein the quad-dominant mesh comprises one or more quadrilaterals, rendering an image based on the quad-dominant mesh; and optimizing the quad-dominant mesh by propagating a loss generated based on the image to the set of machine learning models.