Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervision

TL;DR

This analysis yields two practical insights: (1) self-supervised training on real-world data outperforms supervised training on realistic phantom data, underscoring the superiority of domain realism over ground truth availability; and (2) video frame rate is an extremely important factor for model performance, where dataset-specific video frame sampling is necessary for generating high quality training data.

Abstract

Monocular depth and pose estimation play an important role in the development of colonoscopy-assisted navigation, as they enable improved screening by reducing blind spots, minimizing the risk of missed or recurrent lesions, and lowering the likelihood of incomplete examinations. However, this task remains challenging due to the presence of texture-less surfaces, complex illumination patterns, deformation, and a lack of in-vivo datasets with reliable ground truth. In this paper, we propose **PRISM** (Pose-Refinement with Intrinsic Shading and edge Maps), a self-supervised learning framework that leverages anatomical and illumination priors to guide geometric learning. Our approach uniquely incorporates edge detection and luminance decoupling for structural guidance. Specifically, edge maps are derived using a learning-based edge detector (e.g., DexiNed or HED) trained to capture thin and high-frequency boundaries, while luminance decoupling is obtained through an intrinsic decomposition module that separates shading and reflectance, enabling the model to exploit shading cues for depth estimation. Experimental results on multiple real and synthetic datasets demonstrate state-of-the-art performance. We further conduct a thorough ablation study on training data selection to establish best practices for pose and depth estimation in colonoscopy. This analysis yields two practical insights: (1) self-supervised training on real-world data outperforms supervised training on realistic phantom data, underscoring the superiority of domain realism over ground truth availability; and (2) video frame rate is an extremely important factor for model performance, where dataset-specific video frame sampling is necessary for generating high quality training data.

Figures (6)

• Figure 1: An overview of our proposed self-supervised monocular depth estimation framework. Our key contribution is to explicitly guide the DepthNet with priors from two parallel branches: a LumNet, inspired by IID-SfMLearner, to extract an illumination-aware luminance map ($L_t$), and an EdgeNet, based on DexiNed, to extract a high-fidelity structural edge map ($E_t$). The inputs to the DepthNet are the concatenation of the raw image $I_t$, the luminance map $L_t$, and the edge map $E_t$.
• Figure 2: Qualitative evaluation of generalization on real-world endoscopic video frames with challenging improvements boxed in red.
• Figure 3: Qualitative comparison of estimated camera trajectories across four models on three representative sequences from the C3VD dataset. Each 3D plot shows the predicted path color-coded by temporal order.
• Figure 4: Qualitative comparison of ablation variants on four subsequences from the EndoMapper dataset. Each row shows a sample frame and the corresponding predicted depth maps from different prior configurations.
• Figure 5: Qualitative trajectory comparison under modality-only vs. modality + edge-guided SSIM training on cecum_t4b, sigmoid_t3b and trans_t4b.