BalanceGS: Algorithm-System Co-design for Efficient 3D Gaussian Splatting Training on GPU

• URL: http://arxiv.org/abs/2510.14564v1
• Date: Thu, 16 Oct 2025 11:16:58 GMT
• Title: BalanceGS: Algorithm-System Co-design for Efficient 3D Gaussian Splatting Training on GPU
• Authors: Junyi Wu, Jiaming Xu, Jinhao Li, Yongkang Zhou, Jiayi Pan, Xingyang Li, Guohao Dai,
• Abstract summary: 3DGS has emerged as a promising 3D reconstruction technique.<n>We introduce BalanceGS, the algorithm-system co-design for efficient training in 3DGS.<n>Our approach achieves a 1.44$times$ training speedup on a NVIDIA A100 GPU with negligible quality degradation.
• Score: 14.147819226084627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising 3D reconstruction technique. The traditional 3DGS training pipeline follows three sequential steps: Gaussian densification, Gaussian projection, and color splatting. Despite its promising reconstruction quality, this conventional approach suffers from three critical inefficiencies: (1) Skewed density allocation during Gaussian densification, (2) Imbalanced computation workload during Gaussian projection and (3) Fragmented memory access during color splatting. To tackle the above challenges, we introduce BalanceGS, the algorithm-system co-design for efficient training in 3DGS. (1) At the algorithm level, we propose heuristic workload-sensitive Gaussian density control to automatically balance point distributions - removing 80% redundant Gaussians in dense regions while filling gaps in sparse areas. (2) At the system level, we propose Similarity-based Gaussian sampling and merging, which replaces the static one-to-one thread-pixel mapping with adaptive workload distribution - threads now dynamically process variable numbers of Gaussians based on local cluster density. (3) At the mapping level, we propose reordering-based memory access mapping strategy that restructures RGB storage and enables batch loading in shared memory. Extensive experiments demonstrate that compared with 3DGS, our approach achieves a 1.44$\times$ training speedup on a NVIDIA A100 GPU with negligible quality degradation.

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