The advent of 3D Gaussian Splatting (3D-GS) techniques and their dynamic scene modeling variants, 4D-GS, offers promising prospects for real-time rendering of dynamic surgical scenarios. However, the prerequisite for modeling dynamic scenes by a large number of Gaussian units, the high-dimensional Gaussian attributes and the high-resolution deformation fields, all lead to serve storage issues that hinder real-time rendering in resource-limited surgical equipment. To surmount these limitations, we introduce a Lightweight 4D Gaussian Splatting framework (LGS) that can liberate the efficiency bottlenecks of both rendering and storage for dynamic endoscopic reconstruction. Specifically, to minimize the redundancy of Gaussian quantities, we propose Deformation-Aware Pruning by gauging the impact of each Gaussian on deformation. Concurrently, to reduce the redundancy of Gaussian attributes, we simplify the representation of textures and lighting in non-crucial areas by pruning the dimensions of Gaussian attributes. We further resolve the feature field redundancy caused by the high resolution of 4D neural spatiotemporal encoder for modeling dynamic scenes via a 4D feature field condensation. Experiments on public benchmarks demonstrate efficacy of LGS in terms of a compression rate exceeding 9 times while maintaining the pleasing visual quality and real-time rendering efficiency. LGS confirms a substantial step towards its application in robotic surgical services.
3D 高斯散射（3D-GS）技术及其动态场景建模变体，4D-GS，为动态手术场景的实时渲染提供了有前景的可能性。然而，为了模拟动态场景而需要大量高斯单元、高维高斯属性和高分辨率变形场，这些都导致了严重的存储问题，阻碍了资源有限的手术设备中的实时渲染。为了克服这些限制，我们引入了一个轻量级4D高斯散射框架（LGS），该框架可以解放动态内窥镜重建的渲染和存储效率瓶颈。具体来说，为了最小化高斯数量的冗余，我们提出了基于变形感知的剪枝，通过评估每个高斯对变形的影响来实施。同时，为了减少高斯属性的冗余，我们通过剪减高斯属性的维度，简化了非关键区域的纹理和光照的表达。我们进一步通过4D神经时空编码器的高分辨率特征场来解决特征场冗余问题，采用了4D特征场的压缩。在公共基准测试上的实验表明，LGS在保持令人满意的视觉质量和实时渲染效率的同时，压缩率超过9倍。LGS确认了其在机器人手术服务中应用的重要进步。