3D Shape Generation With Latent Diffusion and Hierarchical T-SDF Decoding
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Solution Overview
Problem
Existing 3D shape generation methods require significant expertise and effort, and traditional representations like point clouds, voxels, and meshes are inefficient and inflexible for generating high-quality 3D shapes.
Innovation Solution
A latent diffusion model and hierarchical autoencoder approach is used to generate 3D shapes from text prompts and low-resolution shape occupancy maps, employing a latent diffusion model to produce multi-scale latent features and a hierarchical autoencoder to decode these features into compact and flexible volumetric Truncated-Signed Distance Field (T-SDF) representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional representations (point clouds, voxels, meshes) are used for 3D shape generation, then the method is easier to implement, but the computational efficiency and flexibility are insufficient
Solution Approach 1:
The patent introduces latent features as an intermediary representation between the input text prompt and the final 3D shape. The latent diffusion model generates multi-scale latent features that capture essential shape information in a compressed, flexible format, which then serves as input to the hierarchical autoencoder for generating the final T-SDF representation. This intermediary latent space enables efficient computation while maintaining shape quality.
Solution Approach 2:
The patent transitions from traditional 3D representations (point clouds, voxels, meshes) to a latent feature space that operates in a different dimensional representation. The hierarchical autoencoder decodes latent features into volumetric T-SDF representations, effectively moving the generation process to a latent dimension where computations are more efficient, then mapping back to the target 3D space.
2Adaptability or versatility
If traditional 3D representations are used, then the implementation is simpler, but the flexibility for generating high-quality shapes with irregular topologies is limited
Solution Approach 1:
The patent changes the representation parameters from traditional geometric data structures (points, voxels, faces) to a continuous latent feature space with multi-scale hierarchical structure. This parameter transformation enables the model to naturally represent irregular topologies and complex geometries that are difficult to capture with traditional discrete representations, while maintaining high generation quality through the hierarchical decoding process.
Solution Approach 2:
The patent segments the shape representation into multiple scales through the hierarchical autoencoder architecture. The multi-scale latent features are decoded at different hierarchical levels, allowing the model to capture both global shape structure and local geometric details. This segmentation enables flexible representation of irregular topologies while maintaining precision through multi-level detail reconstruction.
3Ease of operation
If expert knowledge is required for 3D shape generation, then the output quality may be higher, but the ease of operation and accessibility is reduced
Solution Approach 1:
The patent implements a self-service system where the latent diffusion model and hierarchical autoencoder automatically generate high-quality 3D shapes from text prompts without requiring user expertise. The model autonomously handles the complex transformations from text to latent features to final 3D representation, eliminating the need for manual intervention or expert knowledge while maintaining consistent output quality through the learned generation pipeline.
4Manufacturing precision
If high-resolution 3D shape representation is generated directly, then the shape quality is better, but the computational cost increases significantly
Solution Approach 1:
The patent performs preliminary action by generating multi-scale latent features at lower computational cost before final decoding. The latent diffusion model first creates compressed latent representations that capture essential shape information, which are then fed to the hierarchical autoencoder for final high-resolution T-SDF generation. This preliminary latent feature generation reduces overall computational cost while maintaining high shape representation accuracy.
Solution Approach 2:
The patent employs a nested structure where the hierarchical autoencoder processes latent features through multiple hierarchical levels. The multi-scale latent features are nested within the hierarchical structure, allowing progressive decoding from coarse to fine details. This nesting enables efficient computation by processing shape information at multiple resolutions simultaneously, reducing total computational cost while achieving high final accuracy.
Data Source
AI summary
In some embodiments, a computing system receives an input prompt describing a 3-dimensional (3D) object. The computing system generates one or more levels of latent features based on the input prompt using a latent diffusion model. The computing system decodes the one or more levels of latent features to generate a 3D shape representation using a hierarchical autoencoder. The computing system generates an output shape based on the 3D shape representation.