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.
翻译:在基于立体镜的小型侵入性外科手术中,密集立体匹配在3D形状恢复、AR、VR和导航任务中发挥着不可或缺的作用。虽然提出了许多深神经网络(DNN)方法,但常规前免费方法在行业中仍然很受欢迎,因为缺少开放源代码附加说明的数据集以及任务专用预先培训的DNN的局限性。在先前免费的立体匹配算法中,没有为MIS设定任何GPU环境中的成功实时算法。本文提议为一般的MIS-5任务建立首个CPU级别实时前免费立体匹配算法。我们在640*480图像上实现了平均17个Hz,使用单一核心CPU(i-5-9400)进行外科外科图像。与此同时,它比受欢迎的ELS得到略微精度的精确度。在纠正立体立体图像时采用了基于补缺距的快速差异搜索算法。在GMAP/FE中建议采用粗度概率和无空间计混合模型,以在不同尺度上评估精确度概率。在选择的精确度测试中,在不精确度测地基缩缩缩缩缩缩缩缩缩度测算法中采用了S。在估算中,在模拟中则以量化测算法度测算法度测测算法度测算法则,用了内部测算法,用不力测算法度测算方法对数值。