Dynamic 3D Gaussian Splatting With Codebook-Based Temporal Offsets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D Gaussian splatting techniques struggle with memory and time complexity when applied to dynamic scenes, making them unsuitable for devices with limited GPU memory, such as portable terminals and headsets.
Innovation Solution
A method and apparatus for dynamic Gaussian splatting that codes canonical 3D Gaussians and time-indexed parameter offsets using a codebook, reducing memory requirements by reconstructing and compressing these elements for efficient 2D image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic 3D GS technique is applied to reflect object movement, then rendering performance for dynamic scenes is improved, but GPU memory requirement increases to tens of gigabytes
Solution Approach 1:
The patent segments the 3D Gaussian parameters into canonical components and time-indexed offset components. The canonical 3D Gaussians represent the base scene structure, while the parameter offsets capture temporal variations. This segmentation allows the system to store only the compact canonical representation and small offset corrections, dramatically reducing memory requirements from tens of gigabytes to a fraction of that size.
Solution Approach 2:
The patent extracts the time-dependent variations from the full 3D Gaussian parameters and represents them as separate parameter offsets. By taking out the temporal variation component and representing it as differences from a canonical reference, the system eliminates the need to store complete 3D Gaussian parameters for all time steps, thus reducing GPU memory requirements while maintaining rendering quality.
2Reliability
If dynamic 3D GS technique is applied to reflect object movement, then rendering performance is improved, but training and inference time complexity increases
Solution Approach 1:
The patent divides the computational task into two segments: training the canonical 3D Gaussians (which captures the majority of the scene structure) and computing small parameter offsets for temporal variations. This segmentation reduces the computational burden during both training and inference, as the system only needs to compute and store small offset values rather than complete 3D Gaussian parameter sets for all time steps.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing the canonical 3D Gaussians during training, which represent the time-invariant portion of the scene. During inference, the system only needs to compute small parameter offsets based on time index, significantly reducing the real-time computational complexity compared to storing and processing complete 3D Gaussian parameters for all time steps.
Data Source
AI summary
A method and apparatus generate a 2-dimensional (2D) image. A method for generating a 2-dimensional (2D) image includes obtaining a time index and a view. The method further includes obtaining first coding indices and a first codebook for canonical 3D Gaussians, wherein the canonical 3D Gaussians are 3D Gaussians corresponding to a reference time index, and represent a 3D space corresponding to the reference time index. The method also includes obtaining second encoding indices and a second codebook for a parameter offset, wherein the parameter offset indicates a difference between the canonical 3D Gaussians and 3D Gaussians for the time index. The method further includes reconstructing the canonical 3D Gaussians based on the first coding indices and the first codebook. The method also includes reconstructing parameter offsets of the 3D Gaussians for the time index based on the second coding indices and the second codebook. The method further includes adding the reconstructed canonical 3D Gaussians and the reconstructed parameter offset to reconstruct the 3D Gaussians for the time index. The method also includes generating a second image for the view based on the reconstructed 3D Gaussians.