ELMGS: 面向3D高斯溅射的基于压缩的内存与计算可扩展性增强方法
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting
AI总结：
针对3D高斯溅射模型可扩展性研究空白，提出结合迭代剪枝、可微量化与熵编码估计器的ELMGS方法，实现内存与计算可扩展性提升，验证了其在资源受限设备部署的潜力。
摘要：
3D模型近来因端到端训练的潜力得到普及，该训练方式最早由Neural Radiance Fields（神经辐射场）提出，近期则由3D Gaussian Splatting（3D高斯溅射）模型推动。后者的一大优势是天然具备训练收敛快、可编辑性高的特点。但由于相关研究仍处于起步阶段，现有文献在该模型的可扩展性方面仍存在空白。本研究提出了一种可同时实现此类模型内存与计算可扩展性的方法。具体而言，我们提出了一种迭代剪枝策略，用于移除模型中编码的冗余信息。我们还在优化策略中加入可微量化与熵编码估计器，以提升模型的可压缩性。我们在主流基准上的实验结果证明了所提方法的有效性，为该方案甚至在资源受限设备上的广泛部署铺平了道路。
英文摘要：
3D models have recently been popularized by the potentiality of end-to-end training offered first by Neural Radiance Fields and most recently by 3D Gaussian Splatting models. The latter has the big advantage of naturally providing fast training convergence and high editability. However, as the research around these is still in its infancy, there is still a gap in the literature regarding the model's scalability. In this work, we propose an approach enabling both memory and computation scalability of such models. More specifically, we propose an iterative pruning strategy that removes redundant information encoded in the model. We also enhance compressibility for the model by including in the optimization strategy a differentiable quantization and entropy coding estimator. Our results on popular benchmarks showcase the effectiveness of the proposed approach and open the road to the broad deployability of such a solution even on resource-constrained devices.
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