4D Tensor Interpolation for Edge-Cloud 3D Rendering
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Solution Overview
Problem
In remote edge-cloud collaboration environments, there is a challenge in supporting real-time sharing of 2D or 3D perception from edge devices due to communication failures, which can result in incomplete transmission of encoded features, necessitating a robust method to reconstruct complete 3D contents/scenes from incomplete information.
Innovation Solution
The proposed solution involves modeling a 3D scene as a 4D tensor, where the first three dimensions represent the X-Y-Z coordinate axes and the fourth dimension corresponds to a channel dimension for encoded features. This allows for interpolation on the 4D tensor to obtain streaming scene information, enabling effective rendering of the 3D scene even with incomplete data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If encoded features are transmitted in remote edge-cloud collaboration environments, then real-time sharing of 3D perception is enabled, but communication failures cause incomplete transmission and loss of information
Solution Approach 1:
The patent transforms the 3D scene into a 4D tensor by adding a temporal dimension. This allows interpolation operations to reconstruct missing spatial information from temporal sequences, effectively recovering incomplete encoded features transmitted through unreliable communication channels.
Solution Approach 2:
The system performs preliminary encoding of the 3D scene into compressed tensor representations before transmission. This pre-processing creates a compact form that can be efficiently transmitted and later reconstructed, reducing the impact of potential transmission failures.
2Manufacturing precision
If complete 3D scene data is transmitted, then rendering quality is improved, but transmission time and bandwidth consumption increase
Solution Approach 1:
The patent extracts only the essential encoded features of the 3D scene into a compressed tensor representation, transmitting only this condensed form rather than complete raw data. This extraction enables efficient transmission while maintaining sufficient information for quality reconstruction through interpolation.
Solution Approach 2:
The system changes the parameter representation from raw 3D data to compressed tensor form with reduced dimensions. This parameter transformation maintains the essential information needed for high-quality rendering while significantly reducing data volume for transmission.
3Loss of information
If interpolation is performed on incomplete tensor data, then complete 3D scene reconstruction is achieved, but computational complexity increases
Solution Approach 1:
By introducing the temporal dimension to create a 4D tensor, the patent enables interpolation operations that can recover missing spatial information from temporal sequences. This dimensional extension provides additional degrees of freedom for reconstruction without requiring complex algorithms.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device, and a computer program product for rendering. The method comprises modeling a three-dimensional (3D) scene as a four-dimensional (4D) tensor, wherein a first dimension, a second dimension, and a third dimension in the 4D tensor correspond to an X-Y-Z coordinate axis in the 3D scene, a fourth dimension represents a channel dimension corresponding to an encoded feature, and the encoded feature is obtained by encoding the 3D scene with an encoder corresponding to a decoder. The method further comprises performing interpolation on the 4D tensor to obtain streaming scene information associated with the 3D scene. The method further comprises rendering the 3D scene on the basis of the streaming scene information. According to the method, a unified and flexible architecture can be provided for edge-cloud 3D collaboration, and a 3D scene representation can be processed effectively and efficiently.