Temporal 3D Gaussian Splat Encoding for Smooth Dynamic Streaming
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Solution Overview
Problem
Existing 3D Gaussian splatting (3DGS) techniques for encoding dynamic 3D content result in visual discontinuities and large data sizes that are not suitable for real-time streaming over existing data networks.
Innovation Solution
A smooth temporally segmented encoding method that models primitives as ephemeral particles with opacity functions, groups them into different bins based on temporal values, and applies varying encoding and compression techniques to reduce data size and optimize streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 3D Gaussian splatting techniques are used to encode dynamic 3D content by training each frame independently, then the encoding process is simple and fast, but visual discontinuity occurs between frames causing flashing artifacts and popping
Solution Approach 1:
The system performs preliminary action by training a initial set of 3D Gaussian splats on a key frame before streaming. This pre-trained foundation allows subsequent frames to be encoded more efficiently while maintaining visual continuity through temporal referencing, resolving the contradiction between encoding speed and visual continuity.
Solution Approach 2:
The system implements continuity of useful action by establishing temporal references between frames, where each frame references the final set of splats from the previous frame. This creates a continuous encoding process that maintains visual continuity while preserving encoding efficiency through incremental updates rather than complete re-encoding.
2Quantity of substance
If four-dimensional Gaussian splatting is used to encode dynamic 3D content with 4D mean and covariance matrix, then redundant definition of primitives spanning multiple frames is reduced, but the encoding size becomes too large for real-time streaming
Solution Approach 1:
The system applies segmentation by dividing the 4D Gaussian splat encoding into separate components: spatial attributes (3D position, shape, color) and temporal attributes (opacity over time). This segmentation allows the encoding to store only necessary temporal information as opacity variations rather than full 4D covariance matrices, significantly reducing encoding size while maintaining the ability to represent primitives spanning multiple frames.
Solution Approach 2:
The system extracts and stores only the essential temporal variation information (opacity changes) from the full 4D Gaussian representation. By taking out only the necessary temporal component rather than storing complete 4D covariance data, the encoding achieves temporal compression suitable for real-time streaming while still representing dynamic content across multiple frames.
3Measurement precision
If complete 3D data is streamed for every frame of dynamic 3D content, then visual quality is maintained, but data transmission volume becomes too large for existing data networks
Solution Approach 1:
The system merges the streaming of 3D Gaussian splat data with temporal referencing information. Instead of streaming complete independent 3D data for each frame, it combines the base splat definition with incremental temporal updates (opacity variations), significantly reducing data volume while maintaining visual quality through the temporal coherence provided by frame-to-frame references.
Solution Approach 2:
The system uses copying by referencing the final set of splats from previous frames rather than re-transmitting complete frame data. Each frame copies the structural information from the previous frame's final splat set and only transmits the necessary updates (opacity changes), dramatically reducing the data volume required for streaming while preserving visual quality.
Data Source
AI summary
A system and associated methods perform smooth temporally segmented encoding of dynamic unstructured spatial data or dynamic three-dimensional (3D) content. The system receives the primitives that define visual changes to the dynamic 3D content across multiple frames. The system determines a first set of primitives that remain unchanged for at least N frames and a second set of primitives that remain unchanged for less than the N frames. The system generates a first data stream that encodes the first set of primitives with temporal values at a reduced frame rate, and generates a second data stream that encodes the second set of primitives without temporal values at the desired frame rate. The system streams the first data stream and the second data stream in response to a request for the dynamic 3D content.