论文标题
立体视频中的实时表面变形恢复
Real-time Surface Deformation Recovery from Stereo Videos
论文作者
论文摘要
手术期间的组织变形可能会显着降低手术导航系统的准确性。在本文中，我们提出了一种方法，以实时估算立体声视频的组织表面变形，该视频能够处理遮挡，光滑的表面和快速变形。我们首先使用立体声匹配方法从立体视频帧中提取深度信息，并生成组织模板，然后通过最大程度地减少ICP，ORB特征匹配和可行的（ARAP）成本来估算获得的模板的变形。主要新颖性是双重的：（1）由于非刚性变形，传统的RANSAC方法很难删除特征匹配的异常值；因此，我们提出了一种新颖的1分兰萨克和重新加权方法，以预选匹配的嵌入式，该匹配物处理光滑的表面和快速变形。 （2）我们根据控制点之间的密集连接提出了一种新颖的ARAP成本函数，以实现有限的迭代次数，以实现更好的平滑性能。算法是为GPU并行计算设计和实现的。实验和体内数据的实验表明，这种方法以15Hz的更新速度起作用，在NVIDIA TITAN X GPU上的准确性小于2.5 mm。
Tissue deformation during the surgery may significantly decrease the accuracy of surgical navigation systems. In this paper, we propose an approach to estimate the deformation of tissue surface from stereo videos in real-time, which is capable of handling occlusion, smooth surface and fast deformation. We first use a stereo matching method to extract depth information from stereo video frames and generate the tissue template, and then estimate the deformation of the obtained template by minimizing ICP, ORB feature matching and as-rigid-as-possible (ARAP) costs. The main novelties are twofold: (1) Due to non-rigid deformation, feature matching outliers are difficult to be removed by traditional RANSAC methods; therefore we propose a novel 1-point RANSAC and reweighting method to preselect matching inliers, which handles smooth surfaces and fast deformations. (2) We propose a novel ARAP cost function based on dense connections between the control points to achieve better smoothing performance with limited number of iterations. Algorithms are designed and implemented for GPU parallel computing. Experiments on ex- and in vivo data showed that this approach works at an update rate of 15Hz with an accuracy of less than 2.5 mm on a NVIDIA Titan X GPU.
