BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching

TL;DR

This paper introduces BDIS, a real-time, CPU-based stereo matching algorithm for minimally invasive surgery, achieving high speed and accuracy without relying on pre-trained neural networks or GPU hardware.

Contribution

The paper presents the first CPU-level real-time prior-free stereo matching method for MIS, combining Bayesian probability and Gaussian models for improved accuracy and speed.

Findings

Achieves 17 Hz processing speed on 640x480 images with a single-core CPU.

Outperforms ELAS in accuracy and has fewer outliers.

Operates effectively in textureless and non-Lambertian surfaces.

Abstract

In stereoscope-based Minimally Invasive Surgeries (MIS), dense stereo matching plays an indispensable role in 3D shape recovery, AR, VR, and navigation tasks. Although numerous Deep Neural Network (DNN) approaches are proposed, the conventional prior-free approaches are still popular in the industry because of the lack of open-source annotated data set and the limitation of the task-specific pre-trained DNNs. Among the prior-free stereo matching algorithms, there is no successful real-time algorithm in none GPU environment for MIS. This paper proposes the first CPU-level real-time prior-free stereo matching algorithm for general MIS tasks. We achieve an average 17 Hz on 640*480 images with a single-core CPU (i5-9400) for surgical images. Meanwhile, it achieves slightly better accuracy than the popular ELAS. The patch-based fast disparity searching algorithm is adopted for the rectified stereo images. A coarse-to-fine Bayesian probability and a spatial Gaussian mixed model were proposed to evaluate the patch probability at different scales. An optional probability density function estimation algorithm was adopted to quantify the prediction variance. Extensive experiments demonstrated the proposed method's capability to handle ambiguities introduced by the textureless surfaces and the photometric inconsistency from the non-Lambertian reflectance and dark illumination. The estimated probability managed to balance the confidences of the patches for stereo images at different scales. It has similar or higher accuracy and fewer outliers than the baseline ELAS in MIS, while it is 4-5 times faster. The code and the synthetic data sets are available at https://github.com/JingweiSong/BDIS-v2.