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Leveraging Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments

Ankan Aich, Yangming Lee

TL;DR

The paper tackles monocular depth estimation in specular endoscopic environments where boundary artifacts on thin tools and transparent fluids hinder self-supervised learning. It introduces a synthesis-to-real adaptation pipeline that uses the Depth Anything V2 synthetic priors and Dynamic Vector Low-Rank Adaptation (DV-LORA) to preserve geometric boundaries while adding only about 1.6M trainable parameters, complemented by high-frequency restoration to recover fine surgical textures. A physically-stratified evaluation protocol on the SCARED dataset is proposed to quantify robustness in high-specularity regimes, revealing improved performance in hard cases. Empirically, the approach achieves state-of-the-art aggregate accuracy (e.g., delta<1.25 ≈ 0.981) and substantial reductions in Squared Relative Error compared to baselines, demonstrating robust, real-time capable depth estimation for robotic surgery. This work highlights the practical impact of leveraging synthetic priors for domain transfer in safety-critical medical imaging tasks and provides a rigorous benchmark for future developments, including the use of $\hat{W} = W_0 + \Lambda_v B \Lambda_u A$ and $L_{total} = L_p + \lambda L_e$ in intentional network design.

Abstract

Accurate Monocular Depth Estimation (MDE) is critical for robotic surgery but remains fragile in specular, fluid-filled endoscopic environments. Existing self-supervised methods, typically relying on foundation models trained with noisy real-world pseudo-labels, often suffer from boundary collapse on thin surgical tools and transparent surfaces. In this work, we address this by leveraging the high-fidelity synthetic priors of the Depth Anything V2 architecture, which inherently captures precise geometric details of thin structures. We efficiently adapt these priors to the medical domain using Dynamic Vector Low-Rank Adaptation (DV-LORA), minimizing the parameter budget while bridging the synthetic-to-real gap. Additionally, we introduce a physically-stratified evaluation protocol on the SCARED dataset to rigorously quantify performance in high-specularity regimes often masked by aggregate metrics. Our approach establishes a new state-of-the-art, achieving an accuracy (< 1.25) of 98.1% and reducing Squared Relative Error by over 17% compared to established baselines, demonstrating superior robustness in adverse surgical lighting.

Leveraging Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments

TL;DR

The paper tackles monocular depth estimation in specular endoscopic environments where boundary artifacts on thin tools and transparent fluids hinder self-supervised learning. It introduces a synthesis-to-real adaptation pipeline that uses the Depth Anything V2 synthetic priors and Dynamic Vector Low-Rank Adaptation (DV-LORA) to preserve geometric boundaries while adding only about 1.6M trainable parameters, complemented by high-frequency restoration to recover fine surgical textures. A physically-stratified evaluation protocol on the SCARED dataset is proposed to quantify robustness in high-specularity regimes, revealing improved performance in hard cases. Empirically, the approach achieves state-of-the-art aggregate accuracy (e.g., delta<1.25 ≈ 0.981) and substantial reductions in Squared Relative Error compared to baselines, demonstrating robust, real-time capable depth estimation for robotic surgery. This work highlights the practical impact of leveraging synthetic priors for domain transfer in safety-critical medical imaging tasks and provides a rigorous benchmark for future developments, including the use of and in intentional network design.

Abstract

Accurate Monocular Depth Estimation (MDE) is critical for robotic surgery but remains fragile in specular, fluid-filled endoscopic environments. Existing self-supervised methods, typically relying on foundation models trained with noisy real-world pseudo-labels, often suffer from boundary collapse on thin surgical tools and transparent surfaces. In this work, we address this by leveraging the high-fidelity synthetic priors of the Depth Anything V2 architecture, which inherently captures precise geometric details of thin structures. We efficiently adapt these priors to the medical domain using Dynamic Vector Low-Rank Adaptation (DV-LORA), minimizing the parameter budget while bridging the synthetic-to-real gap. Additionally, we introduce a physically-stratified evaluation protocol on the SCARED dataset to rigorously quantify performance in high-specularity regimes often masked by aggregate metrics. Our approach establishes a new state-of-the-art, achieving an accuracy (< 1.25) of 98.1% and reducing Squared Relative Error by over 17% compared to established baselines, demonstrating superior robustness in adverse surgical lighting.
Paper Structure (26 sections, 2 equations, 3 figures, 3 tables)

This paper contains 26 sections, 2 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. 3D Perception in Robotic Surgery
  3. Challenges in the Surgical Domain
  4. The Evolution of Vision Backbones
  5. Bridging the Gap: Synthetic Priors and Efficient Adaptation
  6. Contributions
  7. Related Works
  8. Self-Supervised Depth Estimation in Surgery
  9. Foundation Models: Real vs. Synthetic Priors
  10. Methodology
  11. Framework Overview
  12. DepthNet: Injecting Synthetic Priors
  13. Backbone Selection and Freezing
  14. Dynamic Vector Adaptation (DV-LORA)
  15. High-Frequency Restoration
  16. ...and 11 more sections

Figures (3)

  • Figure 1: Qualitative comparison (Hypothesis 1) across difficulty clusters. The rows correspond to Medium (Top), Hard (Middle), and Easy (Bottom) subsets.
  • Figure 2: Qualitative comparison (Hypothesis 2) across difficulty clusters. The rows correspond to Hard (Top), Easy (Middle), and Medium (Bottom) subsets.
  • Figure 3: Qualitative pose estimation comparison on the SCARED dataset. The rows correspond to Sequence 01 and Sequence 02. Our method (Right) significantly reduces trajectory drift compared to the baseline (Left), maintaining tighter alignment with the Ground Truth (Blue).