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Colonoscopy reconstruction is pivotal for diagnosing colorectal cancer.However, accurate long-sequence colonoscopy reconstruction faces three majorchallenges: (1) dissimilarity among segments of the colon due to its meanderingand convoluted shape; (2) co-existence of simple and intricately foldedgeometry structures; (3) sparse viewpoints due to constrained cameratrajectories. To tackle these challenges, we introduce a new reconstructionframework based on neural radiance field (NeRF), named ColonNeRF, whichleverages neural rendering for novel view synthesis of long-sequencecolonoscopy. Specifically, to reconstruct the entire colon in a piecewisemanner, our ColonNeRF introduces a region division and integration module,effectively reducing shape dissimilarity and ensuring geometric consistency ineach segment. To learn both the simple and complex geometry in a unifiedframework, our ColonNeRF incorporates a multi-level fusion module thatprogressively models the colon regions from easy to hard. Additionally, toovercome the challenges from sparse views, we devise a DensiNet module fordensifying camera poses under the guidance of semantic consistency. We conductextensive experiments on both synthetic and real-world datasets to evaluate ourColonNeRF. Quantitatively, our ColonNeRF outperforms existing methods on twobenchmarks over four evaluation metrics. Notably, our LPIPS-ALEX scores exhibita substantial increase of about 67%-85% on the SimCol-to-3D dataset.Qualitatively, our reconstruction visualizations show much clearer textures andmore accurate geometric details. These sufficiently demonstrate our superiorperformance over the state-of-the-art methods.

ColonNeRF: Neural Radiance Fields for High-Fidelity Long-Sequence Colonoscopy Reconstruction