解决内窥镜动态组织重建中的伪影问题，提升视觉质量
SAGS: Self-Adaptive Alias-Free Gaussian Splatting for Dynamic Surgical Endoscopic Reconstruction
- 用自适应注意力机制动态建模组织形变
- 在两个公开数据集上各项指标均优于现有方法
- 适合需要高保真动态场景重建的医疗应用
从内窥镜视频中重建动态组织是机器人辅助手术中的关键技术。神经辐射场（NeRFs）虽显著推进了可变形组织重建，但组织运动仍会导致混叠和伪影，严重影响可视化质量。3D高斯点阵（3DGS）提升了重建效率，但现有方法多侧重渲染速度，忽视关键问题。为此，本文提出SAGS：一种自适应无混叠高斯点阵框架。通过注意力驱动的4维形变解码器，结合3维平滑滤波与2维Mip滤波，有效抑制可变形组织重建中的伪影，更精细捕捉组织运动细节。在两个公开基准数据集EndoNeRF和SCARED上的实验表明，本方法在PSNR、SSIM和LPIPS等指标上均超越当前最优水平，且视觉质量更优。
原文摘要 · Abstract (English)
Surgical reconstruction of dynamic tissues from endoscopic videos is a crucial technology in robot-assisted surgery. The development of Neural Radiance Fields (NeRFs) has greatly advanced deformable tissue reconstruction, achieving high-quality results from video and image sequences. However, reconstructing deformable endoscopic scenes remains challenging due to aliasing and artifacts caused by tissue movement, which can significantly degrade visualization quality. The introduction of 3D Gaussian Splatting (3DGS) has improved reconstruction efficiency by enabling a faster rendering pipeline. Nevertheless, existing 3DGS methods often prioritize rendering speed while neglecting these critical issues. To address these challenges, we propose SAGS, a self-adaptive alias-free Gaussian splatting framework. We introduce an attention-driven, dynamically weighted 4D deformation decoder, leveraging 3D smoothing filters and 2D Mip filters to mitigate artifacts in deformable tissue reconstruction and better capture the fine details of tissue movement. Experimental results on two public benchmarks, EndoNeRF and SCARED, demonstrate that our method achieves superior performance in all metrics of PSNR, SSIM, and LPIPS compared to the state of the art while also delivering better visualization quality.
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