AI 中文总结
针对内窥镜重建中组织运动与外观变化建模难题，提出Endo-TSR，通过傅里叶颜色残差和频谱先验增强可变形高斯泼溅，在EndoNeRF和StereoMIS数据集上取得最高PSNR，实现最先进渲染质量。
AI 中文摘要
内窥镜场景重建需要在恢复精细表面细节的同时，对组织运动和时域外观进行建模。可变形高斯模型提供了显式的轨迹，但其固定的颜色系数缺乏对光度变化的专用时域表示。我们提出了Endo-TSR，该方法在可变形高斯泼溅的基础上，增加了有界傅里叶颜色残差和共享时域频率上的独立平移残差。颜色残差捕获局部外观变化，而Matérn频谱先验则对运动校正进行正则化。在联合图像拟合过程中，多尺度拉普拉斯监督引导组织细节的恢复。在EndoNeRF和StereoMIS数据集上的大量实验证明了最先进的渲染质量，在所有评估序列中取得了最高的峰值信噪比（PSNR）。消融研究表明，在测试的组件添加中，时域外观带来了最大的PSNR增益，而外观和细节监督在固定高斯计数下共同改善了渲染效果。
英文摘要
Endoscopic scene reconstruction requires modeling tissue motion and temporal appearance while recovering fine surface detail. Deformable Gaussian models provide explicit trajectories, but their fixed colour coefficients lack a dedicated temporal representation for photometric changes. We propose Endo-TSR, which augments deformable Gaussian splatting with bounded Fourier colour residuals and independent translation residuals on shared temporal frequencies. The colour residuals capture local appearance changes, while a Matérn spectral prior regularises motion corrections. Multi-scale Laplacian supervision guides tissue-detail recovery during joint image fitting. Extensive experiments on the EndoNeRF and StereoMIS datasets demonstrate state-of-the-art rendering quality, with the highest PSNR across all evaluated sequences. Ablation studies show that temporal appearance yields the largest PSNR gain among the tested component additions, while appearance and detail supervision jointly improve rendering with fixed Gaussian counts.
Comments5 pages, 3 figures, 2 tables
