Abstract
Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point cloud registration. The main objective is to evaluate the feasibility of applying deep learning-based point cloud registration methods for image-to-patient registration in augmented reality-guided surgery. We created a dataset of point clouds from medical imaging and corresponding point clouds captured with a popular AR device, the HoloLens 2. We evaluate three well-established deep learning models in registering these data pairs. While we find that some deep learning methods show promise, we show that a conventional registration pipeline still outperforms them on our challenging dataset.
Abstract (translated)
点云配准利用空间变换对3D点云进行对齐。在计算机视觉领域，该任务非常重要，应用于增强现实（AR）和医学成像等领域。本文探讨了两个研究趋势的交集：将AR集成到图像引导手术和利用深度学习进行点云配准。主要目标是对AR引导手术中应用基于深度学习的点云配准方法的可行性进行评估。我们创建了一个医疗影像的点云数据集以及与HoloLens 2流行AR设备捕获的相应点云数据对。我们评估了三种经过充分训练的深度学习模型在这对数据对上的配准效果。虽然我们发现一些深度学习方法显示出潜力，但我们发现传统的配准流程在我们具有挑战性的数据集上仍然表现出更好的性能。
URL
https://arxiv.org/abs/2405.03314
