Multiresolution Deep Implicit Functions for 3D Shape Reconstruction
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Solution Overview
Problem
Existing techniques for 3D object representation, such as deep implicit functions, lack fine-grained details and global information, making them insufficient for accurate shape completion and reconstruction, especially when dealing with partial observations.
Innovation Solution
The implementation of a Multiresolution Deep Implicit Function (MDIF) that represents 3D objects using a multiresolution hierarchy of grids and latent vectors, allowing for progressive decoding and supporting decoder-only 3D object reconstruction by optimizing latent codes at inference, enabling the model to capture arbitrarily fine details and encode 3D object priors at all levels of detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If global models are used to represent the whole 3D object with a single latent vector, then the model complexity is low and training is efficient, but the representation lacks fine-grained local details
Solution Approach 1:
The patent divides the 3D object representation into multiple local regions, each encoded by separate latent vectors. This segmentation allows the model to capture fine-grained local details in each region while maintaining overall global structure, resolving the contradiction between low complexity and high precision by organizing complexity in a structured hierarchical manner.
Solution Approach 2:
The patent introduces a hierarchical dimension to the latent representation space, organizing latent vectors across multiple levels (global to local). This dimensional organization allows efficient traversal and computation while providing progressively finer detail, enabling the system to achieve high representation precision without proportional increases in overall model complexity.
2Manufacturing precision
If local models are used to divide the 3D space into regions and encode each region with a latent vector, then fine-grained local details are captured, but the model complexity increases and global information is lost
Solution Approach 1:
The patent implements a nested hierarchical structure where local latent vectors are nested within the global latent representation. Each level of the hierarchy contains and refines the representation of the previous level, allowing local details to be captured while maintaining global context through the nested organization. This nesting reduces overall complexity by sharing computational structures across levels.
Solution Approach 2:
The patent merges global and local latent representations through a unified hierarchical framework where information flows from global to local and is integrated at multiple levels. This merging allows the model to maintain global information while incorporating local details, achieving high precision without the full complexity of completely independent local models.
3Manufacturing precision
If hybrid models combining global and local models are used, then both global information and local details are preserved, but the device complexity and training difficulty increase significantly
Solution Approach 1:
The patent implements a dynamic hierarchical latent representation system where the level of detail and number of active latent vectors can be adjusted based on the observation quality and reconstruction needs. This dynamic approach allows the model to adaptively use global or local representations as needed, achieving high precision without always incurring the full complexity of the complete hierarchical structure.
Solution Approach 2:
The patent designs a universal hierarchical latent representation framework that can function in multiple modes (global-only, local-only, or hybrid) depending on the application requirements. This multi-functionality allows the same model structure to achieve high precision across different scenarios without requiring separate specialized models, thereby reducing overall system complexity.
4Adaptability or versatility
If deep implicit functions are used for 3D object representation, then continuous 3D space can be represented, but fine-grained details and shape completion accuracy are insufficient
Solution Approach 1:
The patent segments the continuous 3D space representation into hierarchical latent codes that operate at different resolution levels. This segmentation allows the model to represent continuous space while capturing fine-grained details through the hierarchical structure, improving shape completion accuracy by providing dedicated representations for different spatial frequencies and detail levels.
Data Source
AI summary
A method including generating a first vector based on a first grid and a three-dimensional (3D) position associated with a first implicit representation (IR) of a 3D object, generating at least one second vector based on at least one second grid and an upsampled first grid, decoding the first vector to generate a second IR of the 3D object, decoding the at least one second vector to generate at least one third IR of the 3D object, generating a composite IR of the 3D object based on the second IR of the 3D object and the at least one third IR of the 3D object, and generating a reconstructed volume representing the 3D object based on the composite IR of the 3D object.