Neural Pixel Generation With Multiresolution Hash Grids
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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 can be 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
1Extent of automation
If traditional techniques are used to generate 3D models, then automation is achieved, but artifacts appear due to entangled illumination, shading, and surface textures
Solution Approach 1:
The patent segments the 3D model generation process into distinct components: illumination estimation, shading calculation, and surface texture extraction. By processing these elements separately rather than as a unified automated pipeline, the system avoids the artifact generation problem while maintaining automation benefits.
Solution Approach 2:
The patent extracts and separates the entangled components of illumination, shading, and surface textures from the automated generation process. By taking out these components for individual processing and analysis, the system eliminates the artifacts that arise from their entanglement in traditional automated techniques.
2Manufacturing precision
If manual correction is applied to fix artifacts, then model accuracy improves, but complexity increases due to inseparable contributions from illumination, shading, and surface textures
Solution Approach 1:
The patent segments the correction process by separately estimating illumination, calculating shading, and extracting surface textures. This segmentation transforms a complex inseparable correction task into manageable independent components, reducing the overall complexity while improving accuracy.
Solution Approach 2:
The patent extracts the entangled contributions of illumination, shading, and surface textures into separate analyzable components. This extraction enables targeted correction of each element without dealing with their combined complexity, thereby improving model accuracy while reducing correction process complexity.
3Manufacturing precision
If high computational resources are used to generate detailed 3D models, then model detail improves, but resource consumption increases significantly
Solution Approach 1:
The patent segments the computational workload into distinct tasks for illumination estimation, shading calculation, and texture extraction. This segmentation allows for optimized resource allocation to each specific task, achieving detailed 3D models while reducing overall computational resource consumption compared to unified high-resource approaches.
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.