Dynamic 3D Scene Rendering With Canonical Radiance Fields
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generating synthetic images of dynamic scenes with fine-grained detail is complex and computationally burdensome, requiring manual rigging of 3D models and extensive training data, and existing methods struggle with real-time operation and generalization.
Innovation Solution
A method using radiance field parameterizations, such as neural networks, to compute images of dynamic scenes by transforming samples into a canonical space and applying weighted combinations of learned radiance fields, enabling control over deformation and fine-grained features with limited training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rigged 3D models are used to compute synthetic images of dynamic scenes, then fine-grained detail can be captured, but the process becomes complex and time-consuming requiring manual work
Solution Approach 1:
The patent replaces traditional manual rigging and animation systems with a machine learning model that automatically generates rigged 3D models and computes dynamic scene images. The ML model learns from training data to perform tasks that previously required complex manual configuration, thereby reducing device complexity while maintaining fine-grained detail capability
Solution Approach 2:
The system performs preliminary training of the machine learning model using training data comprising images of the scene from multiple viewpoints. This preliminary action prepares the model to automatically handle fine-grained detail extraction and model rigging without requiring manual intervention during actual scene computation
2Reliability
If traditional methods are used to generate synthetic images of dynamic scenes, then realistic details can be achieved, but real-time operation and generalization are difficult
Solution Approach 1:
The patent implements a dynamic system where the machine learning model can adapt to different scenes and conditions. The model is trained on diverse training data and can generalize to new scenes, enabling real-time operation with realistic details. The system dynamically processes input images and generates synthetic images on-demand without requiring pre-computed rigid models for each specific scene
3Measurement precision
If extensive training data is used to train radiance field parameterizations, then accurate radiance field representation is achieved, but data requirements and computational burden increase
Solution Approach 1:
The patent transforms the radiance field representation by parameterizing it in terms of scene geometry, material properties, and lighting conditions. This parameterization allows the system to achieve accurate radiance field representation with fewer training samples, as the parameters capture the essential variations in the scene rather than requiring exhaustive training data coverage
Data Source
AI summary
A cage of primitive 3D elements and associated animation data is received. Compute a ray from a virtual camera through a pixel into the cage animated according to the animation data and compute a plurality of samples on the ray. Compute a transformation of the samples into a canonical cage. For each transformed sample, query a plurality of learnt radiance field parameterizations, each learnt on a different deformed state of the 3D scene to obtain color values for each learnt radiance field. For each transformed sample, query a learnt radiance field parameterization of the 3D scene to obtain an opacity value. Compute, for each transformed sample, a weighted combination of the color values, wherein the weights are related to the local features. A volume rendering method is applied to the weighted combinations of the color and the opacity values producing a pixel value.