Neural Rendering with Invertible Shear Rotation
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Solution Overview
Problem
Conventional neural rendering methods require 3D information during training, complex rendering priors, or expensive runtime decoding schemes, and are not equivariant with respect to general transformations, limiting their ability to generate three-dimensional representations from two-dimensional images without depth information.
Innovation Solution
The development of a machine learning model that enforces equivariance through latent 3D tensor representations, allowing training without 3D supervision and generating implicit three-dimensional representations from two-dimensional images, using equivariance constraints and invertible shear rotations to achieve rotational invariance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional neural rendering methods are used, then training can be performed, but 3D information and complex rendering priors are required, increasing device complexity and computational resources
Solution Approach 1:
The patent extracts and removes the requirement for explicit 3D supervision and complex rendering priors from the training process. By using equivariant neural networks that can infer 3D representations from 2D images alone, the system eliminates the need for additional 3D data inputs and complex prior knowledge, thereby simplifying the overall system while maintaining rendering capability
Solution Approach 2:
The equivariant neural network architecture serves multiple functions simultaneously: it performs image rendering, infers 3D representations, and maintains rotational equivariance all within a single unified model. This multi-functionality eliminates the need for separate systems for 3D processing and rendering, reducing device complexity while improving ease of training
2Productivity
If conventional neural rendering methods are used, then rendering can be performed, but expensive runtime decoding schemes are required, increasing computational resources
Solution Approach 1:
The neural network is pre-trained to directly output rendered images from 2D inputs using equivariant transformations. This preliminary training eliminates the need for expensive runtime decoding schemes, as the model has already learned the optimal mappings during training. The system can now perform rapid inference without requiring computationally intensive post-processing or decoding operations
Solution Approach 2:
The patent replaces expensive mechanical decoding schemes with a neural-based rendering approach. Instead of using traditional rasterization or ray-tracing methods that require significant computational resources, the system uses learned neural representations to directly generate rendered images, substituting complex computational mechanisms with efficient neural network inference
3Manufacturing precision
If conventional neural rendering methods are used, then 3D representations can be generated, but rotational equivariance is not maintained, reducing rendering accuracy
Solution Approach 1:
The patent introduces asymmetric equivariant transformations that specifically account for rotational relationships between views. By designing the neural network to maintain equivariance under rotation, the system creates a symmetric relationship between different viewpoint representations, ensuring that the 3D structure remains consistent and accurate regardless of viewing angle, thereby improving both accuracy and adaptability
Data Source
AI summary
The subject technology provides a framework for learning neural scene representations directly from images, without three-dimensional (3D) supervision, by a machine-learning model. In the disclosed systems and methods, 3D structure can be imposed by ensuring that the learned representation transforms like a real 3D scene. For example, a loss function can be provided which enforces equivariance of the scene representation with respect to 3D rotations. Because naive tensor rotations may not be used to define models that are equivariant with respect to 3D rotations, a new operation called an invertible shear rotation is disclosed, which has the desired equivariance property. In some implementations, the model can be used to generate a 3D representation, such as mesh, of an object from an image of the object.