Constrained 3D Primitive Generation for Fidelity-Preserving Size Reduction
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Solution Overview
Problem
Existing methods for reducing the size of 3D content while maintaining quality suffer from distortions and visual artifacts due to uncontrolled data reduction techniques, which do not account for the constraints of the 3D primitives during generation.
Innovation Solution
A constrained 3D content model training process that generates 3D primitives adhering to specified constraints, such as data types and parameter ranges, to optimize size while preserving fidelity by iteratively comparing against a loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more data is used to encode 3D content (more meshes, points, or Gaussian splats), then quality or fidelity is improved, but size increases
Solution Approach 1:
The patent applies constraints during the 3D content generation process itself, rather than applying deduplication or quantization techniques after generation. By incorporating size constraints into the modeling phase, the system generates content that inherently satisfies both quality and size requirements, avoiding the need for post-processing that introduces artifacts
Solution Approach 2:
The system modifies the generation process by introducing constraint parameters (data types, parameter ranges, size limits) that guide the creation of 3D primitives. These parameter changes enable the model to produce size-optimized content directly, balancing fidelity and size through controlled generation rather than subsequent compression
2Quantity of substance
If deduplication and quantization techniques are applied to reduce 3D content size, then size is reduced, but visual quality deteriorates due to artifacts and distortion
Solution Approach 1:
Instead of applying size-reduction techniques after 3D content generation, the patent incorporates size constraints during the generation process. This preliminary action ensures that the content is created with optimal size characteristics from the start, eliminating the need for post-generation deduplication or quantization that would introduce visual artifacts
Solution Approach 2:
The patent transforms the potential harm of size constraints (which could limit quality) into a benefit by using them as guiding parameters during generation. The constraints shape the generation process to produce content that naturally achieves both size efficiency and visual fidelity, converting what could be a limitation into a design advantage
3Quantity of substance
If lossless deduplication and quantization are applied to reduce 3D content size, then some data reduction is achieved, but significant reduction is not possible
Solution Approach 1:
The patent applies size constraints during the 3D content generation process rather than applying lossless deduplication and quantization after generation. This preliminary approach enables significantly greater data reduction because the model learns to generate compact representations inherently, rather than attempting to compress already-generated content without loss
Data Source
AI summary
A three-dimensional (3D) content creation system modifies operation of a radiance field, neural network, and/or other generative artificial intelligence in order to generate 3D content based on constraints that modify the 3D content modeling. The system receives constraints for reducing a first size of a first 3D representation of a 3D object, and generates different sets of 3D primitives with values for one or more parameters of the 3D primitives that satisfy the constraints. The system selects a particular set of 3D primitives that produces a visual representation that differs from the first 3D representation by less than a threshold amount, and presents the particular set of 3D primitives as a size-optimized second 3D representation of the 3D object with a second size that is less than the first size of the first 3D representation.