通过压缩提升3D高斯点云的内存与计算可扩展性
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting
- 迭代剪枝去除冗余信息，提升模型效率
- 引入可微量化与熵编码估计，增强压缩能力
- 适配资源受限设备，推动实际部署
3D高斯点云（3D Gaussian Splatting）因端到端训练、快速收敛和高度可编辑性受到关注。然而，其在内存与计算上的可扩展性仍存不足。本文提出一种联合压缩方法，通过迭代剪枝移除冗余信息，并在优化中引入可微量化与熵编码估计，以增强模型压缩性。实验在多个基准上验证了该方法的有效性，显著提升了模型的可扩展性，为在资源受限设备上的广泛应用铺平道路。
原文摘要 · Abstract (English)
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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