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.

通过引入轻量级 4D 高斯光斑框架（LGS），该研究解决了在资源有限的手术设备中妨碍实时渲染的存储问题，并在动态内窥镜重建中展现出较高的压缩率、良好的视觉质量和实时渲染效率。