Shared GPU Rasterizer for Real-Time 3D Gaussian Splatting
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Solution Overview
Problem
Efficient acceleration of 3D Gaussian splatting in computer hardware remains a challenge, particularly for edge devices with stringent area and power constraints, as current GPU mechanisms fail to achieve real-time rendering rates and deep network training.
Innovation Solution
Leveraging enhancements to the rasterizer within GPUs to accelerate 3D Gaussian splatting, utilizing shared resources for both triangle and Gaussian rendering, with minimal additional silicon overhead, ensuring compatibility with existing GPU architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 3D Gaussian splatting is computed on conventional GPUs, then rendering capability is provided, but real-time rendering rates (60 FPS) and deep network training are not achieved
Solution Approach 1:
The rendering pipeline is divided into distinct hardware modules: a rasterizer module for Gaussian primitive processing, a triangle mesh rasterizer for traditional mesh rendering, and a compute module for deep network training. This segmentation allows each module to be optimized independently, with the rasterizer dedicated to high-speed Gaussian rendering and the compute module handling training workloads, thereby achieving real-time rendering rates while maintaining training capability.
Solution Approach 2:
The hardware accelerator is designed with multi-functionality to handle both 3D Gaussian splatting rendering and deep network training operations. The compute module can dynamically switch between rendering tasks and training tasks, allowing the same hardware resource to serve multiple purposes. This universality improves productivity by eliminating the need for separate dedicated hardware while maintaining real-time performance through efficient resource utilization.
2Productivity
If dedicated hardware accelerators are used for 3D Gaussian splatting, then rendering speed is improved, but hardware area overhead and system complexity increase
Solution Approach 1:
The patent merges the Gaussian rasterizer and triangle mesh rasterizer into a single integrated rasterizer module that can handle both types of primitives. By combining these functions and sharing common resources such as vertex processing units, texture units, and output merging logic, the design achieves high rendering speed for 3D Gaussian splatting while minimizing hardware area overhead. The unified architecture reduces duplication of functional blocks and optimizes resource utilization.
Solution Approach 2:
The integrated rasterizer is designed as a universal module that can process both Gaussian primitives and triangle mesh primitives. This multi-functionality allows the hardware to accelerate 3D Gaussian splatting rendering without requiring completely separate dedicated hardware, thereby improving rendering speed while keeping device complexity and area overhead manageable through shared resources.
3Ease of operation
If edge GPU mechanisms are used, then portability is maintained, but real-time rendering rates and deep network training performance are insufficient
Solution Approach 1:
The hardware accelerator incorporates dynamic resource allocation and configuration capabilities that allow it to adapt to different workload requirements. The compute module can dynamically switch between rendering and training modes, and the rasterizer can adjust its processing priorities based on real-time demands. This dynamic behavior enables the portable edge device to achieve real-time rendering rates and deep network training performance by optimizing resource utilization according to current tasks.
Solution Approach 2:
The edge GPU architecture is segmented into specialized modules: a rasterizer for high-speed Gaussian rendering, a triangle mesh rasterizer for traditional graphics, and a compute module for general-purpose computing and deep network training. This segmentation allows each module to be optimized for its specific function while working together in a coordinated manner, enabling the portable device to achieve real-time rendering rates and training performance without sacrificing portability.
Data Source
AI summary
Rasterizers that include multiple processing elements each including logic specific to triangle rasterization, logic specific to Gaussian rasterization, and logic common to both of the triangle rasterization and the Gaussian rasterization. The rasterizer further includes a runtime-activated selector to activate either the logic specific to triangle rasterization or the logic specific to Gaussian rasterization.