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This research introduces Endo-TSR, a novel method for endoscopic scene reconstruction that enhances deformable Gaussian splatting by explicitly modeling temporal appearance changes and motion dynamics. By incorporating bounded Fourier color residuals and Matern spectral priors, the approach achieves state-of-the-art rendering quality on standard datasets while recovering fine tissue details under deformation. The work represents a significant advancement in medical computer vision, specifically addressing the photometric challenges inherent in reconstructing moving biological tissues.
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Endo-TSR: Temporal Spectral Modeling of Appearance and Motion for Endoscopic Reconstruction

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.32399v1 Announce Type: cross Abstract: 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 Mat\'ern 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.

Abstract: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.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2609.32399 [cs.CV]

(or arXiv:2609.32399v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.32399 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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