Neural Pixel Generation for Artifact-Reduced 3D Modeling
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Solution Overview
Problem
Generating 3D models of objects is complex and often results in artifacts due to entangled illumination, shading, and surface textures, requiring manual correction, which is complicated by inseparable contributions from these factors.
Innovation Solution
A multiresolution hash grid structure is used to partition video frames, training neural networks to generate 3D models by interpolating features from multiple pixel grids of varying resolutions, disentangling diffuse and color residual components, and optimizing a loss function using numerical gradients to refine the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated techniques are used to generate 3D models, then productivity is improved, but manufacturing precision deteriorates due to artifacts from entangled illumination, shading, and surface textures
Solution Approach 1:
The patent segments the 3D model generation process into multiple independent components: illumination estimation, shading computation, and surface texture extraction. By processing these elements separately rather than as a unified automated pipeline, the system achieves both automation and precision, eliminating artifacts caused by entangled processing while maintaining high productivity through systematic decomposition of the complex generation task.
2Manufacturing precision
If manual correction is applied to fix artifacts, then manufacturing precision is improved, but loss of time increases due to complicated correction process
Solution Approach 1:
The system performs self-correction by automatically disentangling and processing illumination, shading, and texture components through dedicated computational modules. The illumination estimation module independently computes lighting effects, which are then used by the shading computation module to correct artifacts without requiring manual intervention. This self-service mechanism eliminates time-consuming manual correction while maintaining high model quality.
3Device complexity
If traditional processing methods are used, then device complexity is reduced, but object-generated harmful factors increase due to artifacts from entangled information
Solution Approach 1:
The patent introduces a segmented processing architecture that divides the 3D model generation into distinct modules: an illumination estimation module that separates lighting effects, a shading computation module that processes surface interactions, and a texture extraction module that isolates surface properties. This segmentation eliminates artifacts caused by entangled information processing while managing complexity through modular design, where each module handles a specific aspect independently.
Solution Approach 2:
The patent introduces intermediate computational representations that mediate between raw input data and final 3D model output. These intermediates include separated illumination maps, shading coefficients, and texture descriptors that act as intermediary structures to prevent artifact propagation. The intermediary modules process and clean data at each stage, eliminating harmful artifacts before they affect the final model quality.
Data Source
AI summary
Apparatuses, systems, and techniques to generate pixels based on other pixels. In at least one embodiment, one or more neural networks are used to generate one or more pixels based, at least in part, on sets of pixels surrounding the one or more pixels.