3D Object Rendering with Pre-Constructed Feature Grid Selection
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Solution Overview
Problem
Existing rendering techniques, including implicit neural rendering and traditional methods, are computationally expensive, require significant manual effort, and lack flexibility in generating and customizing three-dimensional objects and scenes, especially when dealing with multiple objects or complex selection processes.
Innovation Solution
A computer-implemented method using a meta-storage component with pre-constructed grids of three-dimensional data points, allowing for rapid, training-free generation of implicit neural assets by querying and combining elastic data grids with a selector component to generate rendered images of three-dimensional objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rendering techniques are used, then visual quality can be achieved, but computational cost and manual effort increase significantly
Solution Approach 1:
The patent pre-trains a neural network model on a large dataset of 3D objects to learn generalizable features and representations. This preliminary training enables the model to generate high-quality rendered images of new objects without requiring object-specific training, thus achieving visual quality while reducing computational cost for individual asset generation.
Solution Approach 2:
The patent uses a neural network to learn and copy the essential visual features and rendering characteristics from a training dataset of 3D objects. Once trained, the model can generate realistic rendered images by copying learned patterns and features, eliminating the need for expensive traditional rendering pipelines for each new object.
2Adaptability or versatility
If conventional implicit neural rendering models are used, then continuous volumetric representation is achieved, but data requirements and computational expense increase
Solution Approach 1:
The patent creates a universal neural network model that can represent and render multiple different 3D objects using a single trained model. This universal model learns generalizable features from diverse training data, enabling it to handle various object types without requiring separate training for each object, thus reducing overall data requirements while maintaining volumetric representation capability.
Solution Approach 2:
The patent uses parameterized representations and learns to manipulate key parameters that control object appearance and geometry. By learning to adjust these parameters based on input data, the model can generate diverse 3D representations without requiring exhaustive training data for every possible object configuration.
3Quantity of substance
If spatially localised feature grids are used, then feature storage is achieved, but spatial flexibility and selection capability are limited
Solution Approach 1:
The patent replaces static, rigid feature grids with dynamic neural network representations that can adapt their structure and content based on the specific object being represented. This dynamic approach allows the model to flexibly allocate computational resources to relevant spatial regions and adapt to different object geometries, overcoming the spatial rigidity of traditional feature grids.
Solution Approach 2:
The patent introduces a neural network as an intermediary between the input data and the feature representation. This neural intermediary learns to transform and organize features in a flexible, continuous space, enabling sophisticated selection and manipulation of spatial features without being constrained by fixed grid structures.
4Productivity
If AI-based asset generation models are used, then asset creation speed is improved, but the ability to generate implicit neural models is lost
Solution Approach 1:
The patent replaces traditional explicit asset generation methods (point clouds, meshes) with a neural network-based implicit representation system. This substitution maintains fast generation speeds while preserving the ability to create implicit neural models that can be continuously differentiated and manipulated, enabling both speed and model flexibility.
Data Source
AI summary
A computer-implemented method for generating a rendered image of a three-dimensional object. A meta-storage component is used, that contains at least two pre-constructed grids of three-dimensional data grid points corresponding to features of three-dimensional visual representations of a plurality objects or scenes. A selection code is received, that represents the shape and appearance of the three-dimensional object. An instantiation of the three-dimensional object is constructed, using a selector component, by querying the meta storage component using the selection code to retrieve at least one combination of at least two pre-constructed grids of three-dimensional data grid points from the meta-storage component. A rendered image of the three-dimensional object is then generated using the instantiation of the three-dimensional object.