In recent times, the utilization of 3D models has gained traction, owing to
the capacity for end-to-end training initially offered by Neural Radiance
Fields and more recently by 3D Gaussian Splatting (3DGS) models. The latter
holds a significant advantage by inherently easing rapid convergence during
training and offering extensive editability. However, despite rapid
advancements, the literature still lives in its infancy regarding the
scalability of these models. In this study, we take some initial steps in
addressing this gap, showing an approach that enables both the memory and
computational scalability of such models. Specifically, we propose "Trimming
the fat", a post-hoc gradient-informed iterative pruning technique to eliminate
redundant information encoded in the model. Our experimental findings on widely
acknowledged benchmarks attest to the effectiveness of our approach, revealing
that up to 75% of the Gaussians can be removed while maintaining or even
improving upon baseline performance. Our approach achieves around 50$\times$
compression while preserving performance similar to the baseline model, and is
able to speed-up computation up to 600~FPS.

本研究提出了一种名为 “修剪多余信息” 的后续渐进剪枝技术，旨在解决 3D 模型的内存和计算可扩展性问题，并通过实验证明，在保持或提高基准性能的同时，可以消除高达 75% 的高斯函数。我们的方法在压缩率达到 50 倍的同时，能够加快计算速度高达 600 FPS。