  Within colorectal cancer diagnostics, conventional colonoscopy techniques
face critical limitations, including a limited field of view and a lack of
depth information, which can impede the detection of precancerous lesions.
Current methods struggle to provide comprehensive and accurate 3D
reconstructions of the colonic surface which can help minimize the missing
regions and reinspection for pre-cancerous polyps. Addressing this, we
introduce 'Gaussian Pancakes', a method that leverages 3D Gaussian Splatting
(3D GS) combined with a Recurrent Neural Network-based Simultaneous
Localization and Mapping (RNNSLAM) system. By introducing geometric and depth
regularization into the 3D GS framework, our approach ensures more accurate
alignment of Gaussians with the colon surface, resulting in smoother 3D
reconstructions with novel viewing of detailed textures and structures.
Evaluations across three diverse datasets show that Gaussian Pancakes enhances
novel view synthesis quality, surpassing current leading methods with a 18%
boost in PSNR and a 16% improvement in SSIM. It also delivers over 100X faster
rendering and more than 10X shorter training times, making it a practical tool
for real-time applications. Hence, this holds promise for achieving clinical
translation for better detection and diagnosis of colorectal cancer.
