3D Mesh Reconstruction Using Variational Autoencoder Priors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current multiple view stereovision techniques face challenges in accurately reconstructing 3D surfaces from 2D images due to limitations in handling variations in texture, viewpoint, and lighting conditions, leading to suboptimal mesh reconstruction.
Innovation Solution
The implementation of a system using a variational autoencoder (VAE) to learn data-driven priors for mesh reconstruction, which involves training a machine learning model to encode and decode 2D and 3D meshes, refining the mesh based on geometric constraints from image observations, and subdividing the mesh into smaller meshlets to improve accuracy and generalizability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multiple view stereovision techniques are used to reconstruct 3D surfaces from 2D images, then the reconstruction process can be performed with conventional methods, but the accuracy of mesh reconstruction deteriorates due to limitations in handling variations in texture, viewpoint, and lighting conditions
Solution Approach 1:
The patent transforms the mesh reconstruction problem by changing parameters through learned priors. A variational autoencoder learns to represent meshes in a latent space, where parameters such as vertex positions, normals, and textures are optimized based on image observations. This parameter transformation enables the system to handle variations in texture, viewpoint, and lighting by learning robust representations from training data, thereby improving reconstruction accuracy under diverse conditions.
2Measurement precision
If the mesh is reconstructed using data-driven priors with machine learning models, then the accuracy and generalizability of mesh reconstruction improve, but the device complexity increases due to the need for training and executing machine learning models
Solution Approach 1:
The patent applies preliminary action by pre-training a variational autoencoder on a large dataset of meshes before actual reconstruction. The encoder-decoder architecture learns to compress and reconstruct meshes, capturing essential geometric and topological priors. This preliminary training phase enables the model to generalize to new objects during inference, reducing the need for complex post-processing and improving reconstruction accuracy without requiring complex runtime computations.
Solution Approach 2:
The patent introduces an intermediary latent space between the input images and the output mesh. The variational autoencoder maps image observations to a latent representation that captures essential mesh properties, which then guides the reconstruction process. This intermediary representation simplifies the complex mapping from images to meshes by breaking it down into manageable stages: image encoding, latent space transformation, and mesh decoding, thereby reducing overall system complexity.
3Manufacturing precision
If the mesh is subdivided into smaller meshlets to improve accuracy, then the precision of mesh representation improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the mesh into smaller meshlets or patches that can be independently processed and refined. Each meshlet captures local geometric details, allowing for higher precision representation of complex surfaces. This segmentation enables parallel processing during reconstruction, where each meshlet can be optimized independently based on local image observations, thereby improving overall mesh precision while managing computational complexity through distributed processing.
Data Source
AI summary
One embodiment of a method includes predicting one or more three-dimensional (3D) mesh representations based on a plurality of digital images, wherein the one or more 3D mesh representations are refined by minimizing at least one difference between the one or more 3D mesh representations and the plurality of digital images.