The inaccessibility of user-perceived reality remains an open issue in pursuing the accurate calibration of optical see-through (OST) head-mounted displays (HMDs). Manual user alignment is usually required to collect a set of virtual-to-real correspondences, so that a default or an offline display calibration can be updated to account for the user's eye position(s). Current alignment-based calibration procedures usually require point-wise alignments between rendered image point(s) and associated physical landmark(s) of a target calibration tool. As each alignment can only provide one or a few correspondences, repeated alignments are required to ensure calibration quality. This work presents an accurate and tool-less online OST calibration method to update an offline-calibrated eye-display model. The user's bare hand is markerlessly tracked by a commercial RGBD camera anchored to the OST headset to generate a user-specific cursor for correspondence collection. The required alignment is object-wise, and can provide thousands of unordered corresponding points in tracked space. The collected correspondences are registered by a proposed rotation-constrained iterative closest point (rcICP) method to optimise the viewpoint-related calibration parameters. We implemented such a method for the Microsoft HoloLens 1. The resiliency of the proposed procedure to noisy data was evaluated through simulated tests and real experiments performed with an eye-replacement camera. According to the simulation test, the rcICP registration is robust against possible user-induced rotational misalignment. With a single alignment, our method achieves 8.81 arcmin (1.37 mm) positional error and 1.76 degree rotational error by camera-based tests in the arm-reach distance, and 10.79 arcmin (7.71 pixels) reprojection error by user tests.

中文翻译：

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旋转受限光学透视耳机校准，裸手对准

在追求光学透视 (OST) 头戴式显示器 (HMD) 的准确校准方面，用户感知现实的不可访问性仍然是一个悬而未决的问题。通常需要手动用户对齐来收集一组虚拟到真实的对应关系，以便可以更新默认或离线显示校准以说明用户的眼睛位置。当前基于对齐的校准程序通常需要渲染图像点和目标校准工具的相关物理界标之间的逐点对齐。由于每次比对只能提供一个或几个对应关系，因此需要反复比对以确保校准质量。这项工作提出了一种准确且无需工具的在线 OST 校准方法，用于更新离线校准的眼睛显示模型。用户' 裸手由固定在 OST 耳机上的商用 RGBD 摄像头进行无标记跟踪，以生成用户特定的光标以进行通信收集。所需的对齐是面向对象的，可以在跟踪空间中提供数千个无序的对应点。收集到的对应关系通过提出的旋转约束迭代最近点 (rcICP) 方法进行注册，以优化与视点相关的校准参数。我们为 Microsoft HoloLens 1 实施了这种方法。通过模拟测试和用眼睛置换相机进行的真实实验，评估了所提出的程序对噪声数据的弹性。根据模拟测试，rcICP 配准对于可能的用户引起的旋转错位具有鲁棒性。通过单次对齐，我们的方法达到了 8.81 arcmin (1.