Colonoscopy reconstruction is pivotal for diagnosing colorectal cancer.
However, accurate long-sequence colonoscopy reconstruction faces three major
challenges: (1) dissimilarity among segments of the colon due to its meandering
and convoluted shape; (2) co-existence of simple and intricately folded
geometry structures; (3) sparse viewpoints due to constrained camera
trajectories. To tackle these challenges, we introduce a new reconstruction
framework based on neural radiance field (NeRF), named ColonNeRF, which
leverages neural rendering for novel view synthesis of long-sequence
colonoscopy. Specifically, to reconstruct the entire colon in a piecewise
manner, our ColonNeRF introduces a region division and integration module,
effectively reducing shape dissimilarity and ensuring geometric consistency in
each segment. To learn both the simple and complex geometry in a unified
framework, our ColonNeRF incorporates a multi-level fusion module that
progressively models the colon regions from easy to hard. Additionally, to
overcome the challenges from sparse views, we devise a DensiNet module for
densifying camera poses under the guidance of semantic consistency. We conduct
extensive experiments on both synthetic and real-world datasets to evaluate our
ColonNeRF. Quantitatively, our ColonNeRF outperforms existing methods on two
benchmarks over four evaluation metrics. Notably, our LPIPS-ALEX scores exhibit
a substantial increase of about 67%-85% on the SimCol-to-3D dataset.
Qualitatively, our reconstruction visualizations show much clearer textures and
more accurate geometric details. These sufficiently demonstrate our superior
performance over the state-of-the-art methods.

基于神经辐射场（NeRF）的新重建框架 ColonNeRF，通过重建结构的多分区组合、多级融合以及密集化相机姿势来解决结肠镜检查重建中的形状差异、几何一致性和视角稀疏性等挑战，显著优于现有方法。