Autoregressive 3D Mesh Generation for Realistic Polygonal Objects
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Solution Overview
Problem
Conventional methods for generating 3D object representations using neural networks often result in meshes of varying quality, as mesh reconstruction is treated as a post-processing step, and they fail to effectively account for the unordered elements and discrete face structures present in meshes.
Innovation Solution
An auto-regressive neural network is used to directly generate 3D meshes, accounting for unordered elements and discrete face structures, allowing for the generation of diverse and realistic meshes that can be conditioned on various context inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional neural network approaches are used to generate 3D object representations, then the generation process is simpler, but the mesh quality is poor and varies significantly
Solution Approach 1:
The mesh generation process is segmented into two distinct autoregressive stages: first generating vertex coordinates, then generating face definitions that reference those vertices. This segmentation allows each stage to specialize in handling specific aspects of mesh structure, improving overall mesh quality while maintaining manageable process complexity through modular neural network components.
Solution Approach 2:
The patent transitions from traditional 3D spatial representation to a sequential token-based representation that encodes mesh data in a 1D sequence format. This dimensional transformation enables the application of autoregressive language models to mesh generation, achieving high-quality results by leveraging the strengths of sequence modeling for structured data.
2Manufacturing precision
If mesh reconstruction is performed as a post-processing step, then the neural network generation is simpler, but the resulting meshes are of varying quality at best
Solution Approach 1:
The patent performs mesh reconstruction as a preliminary action within the generation process itself, rather than as a subsequent post-processing step. The autoregressive model directly outputs valid mesh structures with proper vertex-face relationships, ensuring high quality from the start and eliminating the need for separate reconstruction operations that would reduce overall efficiency.
3Reliability
If traditional neural network approaches are used, then the model architecture is simpler, but they fail to account for unordered elements and discrete face structures in meshes
Solution Approach 1:
Instead of trying to process mesh data in its traditional unordered geometric form, the patent inverts the approach by encoding mesh vertices and faces into a sequential token format. This inversion transforms the problem into one that can be solved by autoregressive models, ensuring accurate handling of unordered elements and discrete face structures through the natural sequential processing capability of the model architecture.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating data specifying a three-dimensional mesh of an object using an auto-regressive neural network.