论文标题
BDI：贝叶斯密集的反向搜索方法实时立体手术图像匹配
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching
论文作者
论文摘要
在基于立体镜的微创手术（MIS）中，密集的立体声匹配在3D形状恢复，AR，VR和导航任务中起着必不可少的作用。尽管提出了许多深层神经网络（DNN）方法，但由于缺乏开源注释的数据集以及特定于任务的预训练的DNN的限制，因此在行业中仍然很受欢迎。在先前的无立体声匹配算法中，在没有GPU环境中，MIS中没有成功的实时算法。本文提出了第一个用于一般MIS任务的CPU级实时无立体观念匹配算法。我们在640*480张图像上使用单核CPU（i5-9400）实现平均17 Hz，用于手术图像。同时，它的准确性比流行的Elas略好。基于贴片的快速差异搜索算法用于整流的立体图像。提出了粗到1的贝叶斯概率和空间高斯混合模型，以评估不同尺度的斑块概率。采用可选的概率密度函数估计算法来量化预测方差。广泛的实验表明，提出的方法能够处理由无纹理表面引入的歧义以及非层次反射率和黑暗照明的光度不一致的能力。估计的概率设法平衡了在不同尺度上立体声图像的贴片的信心。它的准确性相似或更高，而在MIS中的基线Elas的距离相似，而离群值较少，而它的速度快4-5倍。代码和合成数据集可在https://github.com/jingweisong/bdis-v2上找到。
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.
