3D Shape Tokenization With Tree-Based Adaptive Latent Encoding
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Solution Overview
Problem
Existing 3D shape generation methods struggle to efficiently encode varying geometric complexities of 3D objects, leading to inefficient compression and degraded performance due to fixed-size latent representations and inadequate handling of sparsity in 3D data.
Innovation Solution
A tree structure-based adaptive tokenization technique that dynamically adjusts latent representations using quadric-error-based subdivision criteria, generating variable-length shape tokens guided by local geometric complexity, and employs a tree structure-based autoregressive generative model for efficient 3D shape generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed-size latent representations are used for 3D shape generation, then the representation is simple and consistent, but the compression efficiency deteriorates and performance degrades due to inability to handle varying geometric complexities
Solution Approach 1:
The patent segments the 3D shape representation into a hierarchical tree structure where each node represents a region of the shape. This segmentation allows different parts of the shape to be represented at different levels of detail, enabling the system to adapt to varying geometric complexities without using a single fixed-size representation for the entire shape.
Solution Approach 2:
The patent applies local quality by assigning different representation granularities to different regions of the 3D shape based on their geometric complexity. Complex regions are represented with finer detail (more tokens) while simple regions use coarser representation, optimizing the balance between adaptability and system complexity.
2Productivity
If uniform tokenization is applied to all 3D shapes, then the processing is simplified, but the compression efficiency deteriorates due to inability to capture sparsity in 3D data
Solution Approach 1:
The patent segments the 3D space into a hierarchical tree structure where each node corresponds to a spatial region. This segmentation enables the system to identify and process only the regions containing surface points (non-empty nodes), thereby capturing sparsity in 3D data and improving compression efficiency without requiring overly complex tokenization mechanisms.
Solution Approach 2:
The patent implements dynamic tokenization by generating variable-length token sequences based on the actual content of each 3D shape. The number and type of tokens generated at each tree level depend on the geometric complexity and sparsity of the shape, allowing the system to adapt the tokenization process to the specific characteristics of each shape rather than using a static uniform approach.
3Quantity of substance
If variable-length latent representations are generated based on local geometry complexity, then the token count is reduced by 50% while maintaining visual quality, but the generation process becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-computing the hierarchical tree structure and assigning tokens to nodes based on geometric complexity analysis performed before the main generation process. This preliminary token assignment establishes a framework that guides the subsequent generative process, reducing the overall complexity of variable-length latent representation generation while achieving significant token count reduction.
Data Source
AI summary
Various implementations relate to methods, systems, and non-transitory computer-readable media for autoregressive shape generation. In some implementations, a computer-implemented method includes obtaining an input mesh corresponding to a three-dimensional (3D) object. The computer-implemented method further includes partitioning the input mesh into a plurality of 3D cells. Each of the plurality of 3D cells may correspond to a respective root node of a plurality of root nodes of a tree structure. The computer-implemented method further includes, for each of the plurality of 3D cells, generating a respective first variable-length latent value that is a function of a surface complexity of a shape located in a corresponding 3D cell.