Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives
This work addresses bottlenecks in 3D scene reconstruction for resource-constrained settings, representing an incremental improvement.
The paper tackled inefficiencies in 3D Gaussian Splatting that limit rendering speed and model size, resulting in a 6.71x average acceleration in rendering speed across multiple datasets.
3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differentiable 3D Gaussians. However, its rendering speed and model size still present bottlenecks, especially in resource-constrained settings. In this paper, we identify and address two key inefficiencies in 3D-GS to substantially improve rendering speed. These improvements also yield the ancillary benefits of reduced model size and training time. First, we optimize the rendering pipeline to precisely localize Gaussians in the scene, boosting rendering speed without altering visual fidelity. Second, we introduce a novel pruning technique and integrate it into the training pipeline, significantly reducing model size and training time while further raising rendering speed. Our Speedy-Splat approach combines these techniques to accelerate average rendering speed by a drastic $\mathit{6.71\times}$ across scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets.