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

While 3D Gaussian Splatting (3DGS) has revolutionized novelview synthesis with its photorealistic and real-time rendering, its lengthytraining process remains a critical bottleneck. Existing acceleration meth-ods largely treat algorithm design and system optimization in isolation,failing to fully unleash hardware potential. To systematically addressthis, we propose LiteGS, a high-performance framework driven by afundamental system-algorithm codesign. At the GPU and data layers,LiteGS introduces a warp-based rasterization paradigm and a Cluster-Cull-Compact pipeline, fundamentally resolving data conflicts and spa-tial locality issues. Empowered by this robust backend, we introduce avariance-guided densification metric at the algorithm layer to accuratelyidentify under-reconstructed regions. This powerful synergy establishes anew state-of-the-art: LiteGS consistently achieves the highest renderingquality and the fastest training speed across all parameter scales. Exten-sive evaluations demonstrate our milestone performance. Compared tothe high-quality SOTA (3DGS-MCMC), LiteGS achieves at least a 10×speedup while matching or exceeding its fidelity. Furthermore, by unlock-ing a “space-for-time” aggressive densification strategy, LiteGS success-fully compresses the training time to the sub-minute level on RTX3090(further to ∼30 seconds on RTX 4090) while securing rendering qualitycomparable to the original 3DGS.