This paper reports a CPU-level real-time stereo matching method for surgical images (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed method is built on the fast ''dense inverse searching'' algorithm, which estimates the disparity of the stereo images. The overlapping image patches (arbitrary squared image segment) from the images at different scales are aligned based on the photometric consistency presumption. We propose a Bayesian framework to evaluate the probability of the optimized patch disparity at different scales. Moreover, we introduce a spatial Gaussian mixed probability distribution to address the pixel-wise probability within the patch. In-vivo and synthetic experiments show that our method can handle ambiguities resulted from the textureless surfaces and the photometric inconsistency caused by the Lambertian reflectance. Our Bayesian method correctly balances the probability of the patch for stereo images at different scales. Experiments indicate that the estimated depth has higher accuracy and fewer outliers than the baseline methods in the surgical scenario.
翻译:本文报告了外科图像的CPU级实时立体匹配方法( 10 Hz, 640 * 480 图像, 单核为 i5- 9400 ) 。 提议的方法建在快速“ 强烈反向搜索” 算法上, 该算法估计立体图像的差异。 相重叠的图像补丁( 任意正方形图像段) 根据光度一致性假设对不同比例图像进行校正。 我们提出贝叶西亚框架, 以评价在不同比例上优化补丁差的概率。 此外, 我们引入了空间高斯混合概率分布, 以解决补丁中的像素顺差概率。 校内和合成实验显示, 我们的方法可以处理无纹表面产生的模糊性, 以及Lambertian反射造成的光度不一致性。 我们的巴伊斯方法正确平衡了不同比例上立体图像的补丁的概率。 实验显示, 估计深度比外科情景中的基线法更准确, 外差也更少。