ColonSplat: Reconstruction of Peristaltic Motion in Colonoscopy with Dynamic Gaussian Splatting

TL;DR

ColonSplat is proposed, a dynamic Gaussian Splatting framework that captures peristaltic-like motion while preserving global geometric consistency, achieving superior geometric fidelity on C3VDv2 and DynamicColon datasets.

Abstract

Accurate 3D reconstruction of colonoscopy data, accounting for complex peristaltic movements, is crucial for advanced surgical navigation and retrospective diagnostics. While recent novel view synthesis and 3D reconstruction methods have demonstrated remarkable success in general endoscopic scenarios, they struggle in the highly constrained environment of the colon. Due to the limited field of view of a camera moving through an actively deforming tubular structure, existing endoscopic methods reconstruct the colon appearance only for initial camera trajectory. However, the underlying anatomy remains largely static; instead of updating Gaussians' spatial coordinates (xyz), these methods encode deformation through either rotation, scale or opacity adjustments. In this paper, we first present a benchmark analysis of state-of-the-art dynamic endoscopic methods for realistic colonoscopic scenes, showing that they fail to model true anatomical motion. To enable rigorous evaluation of global reconstruction quality, we introduce DynamicColon, a synthetic dataset with ground-truth point clouds at every timestep. Building on these insights, we propose ColonSplat, a dynamic Gaussian Splatting framework that captures peristaltic-like motion while preserving global geometric consistency, achieving superior geometric fidelity on C3VDv2 and DynamicColon datasets. Project page: https://wmito.github.io/ColonSplat

Figures (5)

• Figure 1: The C3VDv2 dataset contains realistic colonoscopy sequences with substantial non-rigid deformations over time. The top two rows show captures from the endoscopic camera and the corresponding renders produced by different methods. The bottom row illustrates the global structure of the reconstructed colon. Peristaltic-like dynamic challenge baseline approaches; however, ColonSplat uniquely maintains a physically plausible global structure across timesteps. Please zoom in for details, see more examples in supplementary videos.
• Figure 2: ColonSplat reconstructs dynamic 3D anatomy from colonoscopy video using estimated depth. A deformation model updates canonical Gaussians parameters at each time step for consistent dynamic reconstruction. Training uses RGB and depth supervision, with KNN and color regularization, to ensure accurate, artifact-free results.
• Figure 3: Qualitative comparison for C3VDv2 dataset. Please zoom in for details.ColonSplat achieves superior reconstruction quality compared to the baselines.
• Figure 4: DynamicColon. The top row shows renders from test trajectory. The bottom row presents views from cameras positioned outside the colon. Competing methods produce significant artifacts that mimic deformations. ColonSplat accurately captures the 3D structure of the colon without such artifacts, even when viewed from outside.
• Figure 5: Ablation study on C3VDv2 scene. Our proposed components significantly enhance realistic dynamic colon deformation. KNN regularization provides smooth tissue-like Gaussian surface across timesteps while our proposed constraints eliminate geometric artifacts. Please zoom in for details.