Dynamic Endoscopic Gaussian Reconstruction for Vascular Information Enhancement

Reconstructing deformable tissues from endoscopic videos is a critical task in fields such as robot-assisted minimally invasive surgery, virtual reality, and augmented reality. Compared with neural radiance fields, the introduction of the 3D Gaussian Splatting technique (3DGS) significantly reduces the time re-quired for 3D reconstruction and rendering while maintaining good image quality to a certain extent, offering new possibilities for real-time 3D rendering during endoscopic procedures. However, this technique suffers from a signifi-cant loss of image details during rendering, manifesting in endoscopic scenes as missing vascular details, which directly affects the accuracy of lesion local-ization by clinicians. To address this issue, this paper proposes a novel method for enhancing vascular information in dynamic endoscopic scene reconstruc-tion. The proposed approach begins with image preprocessing, employing a lightweight and efficient vascular enhancement algorithm. By optimizing brightness, details, and contrast in the HSV color space, it enhances the visibil-ity of vascular structures. Combined with the state-of-the-art 3DGS method for endoscopy, it improves the display of vascular details in 3D images. Experi-mental results demonstrate that, compared with the original 3D rendering and other 2D enhancement algorithms, the proposed method significantly enhances the visualization quality of vascular structures, effectively compensating for the shortcomings of the 3D Gaussian Splatting technique in vascular detail rep-resentation.