Assess benefits of dedicated GPU texture hardware for textured Gaussian Splatting

Determine whether leveraging dedicated GPU texture hardware capabilities (e.g., hardware texture samplers and caches) yields measurable training or rendering performance improvements for the authors’ textured 2D Gaussian Splatting renderer implemented in CUDA, and ascertain under which platforms and hardware classes (such as WebGL-based renderers and low-end devices) such acceleration is effective.

Background

The paper introduces a content-aware texturing method for 2D Gaussian Splatting that dynamically adapts texel size and manages the number of primitives to balance appearance and geometric complexity. The implementation uses a custom CUDA renderer, with per-primitive texture maps whose resolutions vary during optimization.

While the method demonstrates favorable quality and parameter efficiency compared to alternative textured Gaussian approaches, the authors note that they did not explore the use of dedicated GPU texture hardware. They explicitly state uncertainty regarding the potential performance benefits of such hardware in their CUDA-based implementation and suggest that environments like WebGL and low-end hardware might particularly benefit from hardware acceleration.