LiteGS: a high-performance framework to train 3dgs in subminutes via system and algorithm codesign

Kaimin Liao, Hua Wang, Zhi Chen, Luchao Wang, Yaohua Tang

Keywords: gaussian splatting, acceleration, 3dgs

TL;DR: LiteGS achieves state-of-the-art speed and accuracy in 3D Gaussian Splatting, with up to 13.4× faster training through warp-based rasterization, Morton-coded cluster-culling, and robust densification.

Abstract: 3D Gaussian Splatting (3DGS) has emerged as promising alternative in 3D representation. However, it still suffers from high training cost. This paper introduces LiteGS, a high performance framework that systematically optimizes the 3DGS training pipeline from multiple aspects. At the low-level computation layer, we design a "warp-based raster'' associated with two hardware-aware optimizations to significantly reduce gradient reduction overhead. At the mid-level data management layer, we introduce dynamic spatial sorting based on Morton coding to enable a performant "Cluster-Cull-Compact'' pipeline and improve data locality, therefore reducing cache misses. At the top-level algorithm layer, we establish a new robust densification criterion based on the variance of the opacity gradient, paired with a more stable opacity control mechanism, to achieve more precise parameter growth. Experimental results demonstrate that LiteGS accelerates the original 3DGS training by up to 13.4x with comparable or superior quality and surpasses the current SOTA in lightweight models by up to 1.5x speedup. For high-quality reconstruction tasks, LiteGS sets a new accuracy record and decreases the training time by an order of magnitude.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 6543