3D Mesh Reconstruction via Multi-View Coordinate Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital image processing systems face challenges in accurately reconstructing three-dimensional models from images or video of real-life objects, particularly when dealing with unknown objects or topologies, as they often produce inaccurate meshes due to oversmoothing, occlusion, and incompatibility with downstream applications.
Innovation Solution
A multi-view coordinate alignment system that uses neural networks to generate coordinate-aligned feature vectors, aligning the coordinate systems of multiple images to a single system, and then utilizes these vectors to create a three-dimensional mesh, improving accuracy and flexibility in reconstructing objects without manual alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional systems use point clouds to represent objects and construct meshes, then the reconstruction process can be simplified, but the mesh accuracy deteriorates due to oversmoothing and merging of details
Solution Approach 1:
The patent segments the object representation into multiple coordinate systems, each associated with different views of the object. Instead of using a single point cloud that oversmooths details, the system divides the reconstruction task into multiple coordinate-aligned feature vectors from different perspectives, preserving fine details while maintaining computational tractability.
Solution Approach 2:
The patent transitions from 2D images to 3D reconstruction by introducing coordinate system transformations. Multiple 2D views are mapped to a unified 3D coordinate system through neural network-based coordinate alignment, enabling accurate 3D mesh generation without relying on point cloud intermediates that lose detail.
2Device complexity
If conventional systems use a single viewpoint photometric loss for reconstruction, then the optimization process is simpler, but the accuracy of mapping images to three-dimensional reconstructions deteriorates
Solution Approach 1:
The patent merges multiple viewpoint information into a unified coordinate system. By combining coordinate-aligned feature vectors from multiple images and views, the system achieves accurate reconstruction mapping that leverages information from all perspectives simultaneously, rather than relying on a single viewpoint.
Solution Approach 2:
The patent creates a universal coordinate system that can accommodate multiple views and perspectives. The coordinate alignment neural network learns to transform various viewpoint representations into a common framework, making the reconstruction system versatile and accurate across different imaging conditions and object orientations.
3Device complexity
If conventional systems generate reconstruction geometries in coordinate systems relative to the center of mass, then the object representation is simplified, but compatibility with downstream applications like multi-view stereo and SLAM deteriorates
Solution Approach 1:
The patent implements a dynamic coordinate system selection mechanism. Instead of fixed center-of-mass coordinates, the system can adaptively choose coordinate systems based on the first image or other reference frames, enabling seamless integration with downstream applications that require specific coordinate conventions while maintaining flexible object representation.
Solution Approach 2:
The patent introduces coordinate alignment neural networks as intermediaries between different coordinate systems. These networks transform feature vectors from various coordinate frames into a unified target coordinate system, serving as a mediator that ensures compatibility with downstream applications without losing the simplicity of object-centered representations.
4Manufacturing precision
If conventional systems increase the number of points in a point cloud to reduce mesh inaccuracies, then the mesh accuracy may improve slightly, but the computing resources needed to process the point cloud increase significantly
Solution Approach 1:
The patent replaces the traditional mechanical point cloud construction and meshing process with a neural network-based direct 3D generation approach. Instead of increasing point cloud density and performing computationally intensive mesh construction, the system uses coordinate-aligned feature vectors processed by neural networks to directly generate accurate 3D meshes, reducing computational resource requirements.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for reconstructing three-dimensional meshes from two-dimensional images of objects with automatic coordinate system alignment. For example, the disclosed system can generate feature vectors for a plurality of images having different views of an object. The disclosed system can process the feature vectors to generate coordinate-aligned feature vectors aligned with a coordinate system associated with an image. The disclosed system can generate a combined feature vector from the feature vectors aligned to the coordinate system. Additionally, the disclosed system can then generate a three-dimensional mesh representing the object from the combined feature vector.