3D Gaussian Video Rendering With Spatio-Temporal Embeddings
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Solution Overview
Problem
Existing 3D Gaussian splatting-based simulations fail to effectively render both background and foreground information in a scene due to reliance on spatial information alone, resulting in artifacts and missing details.
Innovation Solution
A method involving 3D Gaussian splatting data generation using spatial and temporal data, combined with neural networks to create spatial-temporal Gaussian embeddings, and offset data to enhance rendering accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If only spatial information is used in 3D Gaussian representations, then the rendering process is simpler and faster, but the simulated results contain artifacts and fail to render important details
Solution Approach 1:
The patent transitions from purely spatial 3D Gaussian representations to spatio-temporal representations by adding the temporal dimension. This is achieved by processing video frames sequentially and propagating Gaussian parameters across time, allowing the system to maintain rendering efficiency while significantly improving accuracy and reducing artifacts through temporal context utilization.
Solution Approach 2:
The patent performs preliminary processing of temporal information by pre-computing temporal offsets and embeddings before the final rendering stage. This allows the system to prepare enhanced Gaussian representations in advance, maintaining fast real-time rendering while incorporating detailed temporal information that improves rendering accuracy and reduces artifacts.
2Device complexity
If only spatial information is used in 3D Gaussian representations, then the data processing is simpler, but important scene details are lost
Solution Approach 1:
The patent adds the temporal dimension to the 3D Gaussian representations, transforming them from purely spatial to spatio-temporal. This allows the system to capture and preserve important scene details that vary over time, such as moving objects and changing environments, without excessively increasing processing complexity through efficient temporal modeling techniques.
Solution Approach 2:
The patent creates a unified spatio-temporal Gaussian representation framework that handles both spatial and temporal information within a single model. This multi-functional approach allows the system to simultaneously preserve scene details and maintain reasonable processing complexity by integrating temporal awareness into the core Gaussian representation rather than adding separate complex processing stages.
Data Source
AI summary
A set of images of a scene re received. Each image includes temporal data and spatial data relating to the scene. Based on the spatial data of each image, three-dimensional (3D) Gaussian splatting data is generated. The temporal data of each image and the 3D Gaussian splatting data are inputted to a neural network to generate spatial-temporal 3D Gaussian embeddings. Offset data based on the spatial-temporal 3D Gaussian embeddings is generated. The video of the scene is rendered based on the 3D Gaussian splatting data and the offset data, allowing for improved rendering of video of the scene.