  Robot-assisted minimally invasive surgery benefits from enhancing dynamic
scene reconstruction, as it improves surgical outcomes. While Neural Radiance
Fields (NeRF) have been effective in scene reconstruction, their slow inference
speeds and lengthy training durations limit their applicability. To overcome
these limitations, 3D Gaussian Splatting (3D-GS) based methods have emerged as
a recent trend, offering rapid inference capabilities and superior 3D quality.
However, these methods still struggle with under-reconstruction in both static
and dynamic scenes. In this paper, we propose HFGS, a novel approach for
deformable endoscopic reconstruction that addresses these challenges from
spatial and temporal frequency perspectives. Our approach incorporates
deformation fields to better handle dynamic scenes and introduces Spatial
High-Frequency Emphasis Reconstruction (SHF) to minimize discrepancies in
spatial frequency spectra between the rendered image and its ground truth.
Additionally, we introduce Temporal High-Frequency Emphasis Reconstruction
(THF) to enhance dynamic awareness in neural rendering by leveraging flow
priors, focusing optimization on motion-intensive parts. Extensive experiments
on two widely used benchmarks demonstrate that HFGS achieves superior rendering
quality. Our code will be available.
