# Adaptive Support Weight-Based Stereo Matching with Iterative Disparity Refinement

## TL;DR

This paper introduces a real-time 3D reconstruction method for minimally invasive surgery that improves depth estimation and could support surgical navigation and augmented reality.

## Contribution

A novel deterministic stereo-matching method with adaptive support weights for endoscopic imaging challenges is proposed and evaluated.

## Key findings

- The method achieves MAEs of 3.79 mm and 3.61 mm on SCARED datasets 8 and 9, with 24.9 FPS.
- On synthetic data, the method reaches an MAE of 140.06 μm and RMSE of 251.9 μm under ideal conditions.
- The approach provides accurate, deterministic depth estimation suitable for clinical applications.

## Abstract

Real-time 3D reconstruction in minimally invasive surgery improves depth perception and supports intraoperative decision-making and navigation. However, endoscopic imaging presents significant challenges, such as specular reflections, low-texture surfaces, and tissue deformation. We present a novel, deterministic and iterative stereo-matching method based on adaptive support weights that is tailored to these constraints. The algorithm is implemented in CUDA and C++ to enable real-time performance. We evaluated our method on the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) dataset and a custom synthetic dataset using the mean absolute error (MAE), root mean square error (RMSE), and frame rate as metrics. On SCARED datasets 8 and 9, our method achieves MAEs of 3.79 mm and 3.61 mm, achieving 24.9 FPS on a system with an AMD Ryzen 9 5950X and NVIDIA RTX 3090. To the best of our knowledge, these results are on par with or surpass existing deterministic stereo-matching approaches. On synthetic data, which eliminates real-world imaging errors, the method achieves an MAE of 140.06 μm and an RMSE of 251.9 μm, highlighting its performance ceiling under noise-free, idealized conditions. Our method focuses on single-shot 3D reconstruction as a basis for stereo frame stitching and full-scene modeling. It provides accurate, deterministic, real-time depth estimation under clinically relevant conditions and has the potential to be integrated into surgical navigation, robotic assistance, and augmented reality workflows.