Point Cloud Splatting for Low-Bandwidth 3D Streaming
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Solution Overview
Problem
Point clouds require significant bandwidth and hardware resources for streaming and processing due to their large data size, making them less desirable for real-time applications.
Innovation Solution
Utilizing adapted splatting techniques with neural networks to generate optimized splats that reduce point cloud data by up to 95% while maintaining visual fidelity, achieved through Gaussian splatting and neural radiance fields to replace sets of points with primitives based on variance in visual characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If point cloud data is used to represent 3D models with high visual fidelity, then visual quality is improved, but data size increases significantly
Solution Approach 1:
The patent uses Gaussian splatting to create simplified 2D projections (splats) that copy and represent the visual appearance of complex 3D point cloud data. Instead of storing and transmitting all original point cloud data, the system creates compact 2D splat representations that can be rendered to reproduce the visual scene, dramatically reducing data size while maintaining visual fidelity.
Solution Approach 2:
The patent extracts only the essential visual information from the point cloud data needed for rendering. By using neural radiance fields and Gaussian splatting, the system extracts and stores only the critical visual characteristics (color, opacity, position) in a compressed format, discarding redundant data while preserving what is necessary for visual reconstruction.
2Manufacturing precision
If point cloud data is streamed with high resolution, then visual fidelity is improved, but bandwidth requirements increase
Solution Approach 1:
The system creates compact 2D splat representations that can be streamed efficiently. These splats are much smaller than equivalent point cloud data but can be rendered to produce high-fidelity visual output, reducing bandwidth requirements while maintaining visual quality.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D splat representations for streaming purposes. This dimensionality reduction allows the data to be compressed and transmitted more efficiently over networks, while the rendering system reconstructs the 3D visual appearance from these 2D projections, achieving bandwidth efficiency without sacrificing visual fidelity.
3Manufacturing precision
If point cloud data is processed with high detail, then visual quality is improved, but processing time increases
Solution Approach 1:
The system pre-processes the point cloud data once to create Gaussian splat representations, which are then quickly rendered and processed. This copying approach shifts the computational burden to an offline preprocessing stage, allowing real-time or near-real-time processing with the compact splat data, thereby reducing processing time while maintaining visual quality.
Solution Approach 2:
The patent performs complex processing operations in advance to create the Gaussian splat representations and neural radiance field models. This preliminary action includes training neural networks and generating splat data during an offline phase, so that during actual use, the system only needs to perform lightweight rendering operations, significantly reducing processing time for real-time applications.
Data Source
AI summary
An optimization system reduces the data encoded within a point cloud for streaming and/or rendering of a lossy representation of the point cloud. The optimization system generates a first optimized splat for a first visual characteristic of the point cloud by replacing a first set of points having a first common value for the first visual characteristic with a first replacement primitive, and generates a second optimized splat for a second visual characteristic of the point cloud by replacing a second set of points having a second common value for the second visual characteristic with a second replacement primitive. The optimization system provides the first optimized splat and the second optimized splat instead of the original points of the point cloud in response to a request to access the point cloud.