Gaussian splatting (GS) for 3D reconstruction has become quite popular due to their fast training, inference speeds and high quality reconstruction. However, GS-based reconstructions generally consist of millions of Gaussians, which makes them hard to use on computationally constrained devices such as smartphones. In this paper, we first propose a principled analysis of advances in efficient GS methods. Then, we propose Trick-GS, which is a careful combination of several strategies including (1) progressive training with resolution, noise and Gaussian scales, (2) learning to prune and mask primitives and SH bands by their significance, and (3) accelerated GS training framework. Trick-GS takes a large step towards resource-constrained GS, where faster run-time, smaller and faster-convergence of models is of paramount concern. Our results on three datasets show that Trick-GS achieves up to 2x faster training, 40x smaller disk size and 2x faster rendering speed compared to vanilla GS, while having comparable accuracy.
3D重建中的高斯点云（Gaussian Splatting, GS）因其快速训练、高效推理和高质量重建而受到广泛关注。然而，GS 生成的重建通常包含数百万个高斯点，使其难以在智能手机等计算资源受限的设备上运行。本文首先对高效 GS 方法的最新进展进行了系统分析。随后，我们提出 Trick-GS，一种精心设计的优化策略组合，包括：（1）渐进式训练，通过逐步调整分辨率、噪声和高斯尺度，提高收敛效率；（2）基于重要性的剪枝与掩码学习，优化高斯原语和球谐（SH）带的存储与计算；（3）加速 GS 训练框架，提升整体训练与推理效率。Trick-GS 迈出了面向资源受限环境（如移动设备）优化 GS 运行的重要一步，使得 GS 具备更快的运行速度、更小的模型尺寸和更快的收敛能力。我们在三个数据集上的实验结果表明，与标准 GS 相比，Trick-GS 训练速度提高最多 2 倍，磁盘存储需求减少 40 倍，渲染速度提升 2 倍，同时保持了相当的重建精度。