Accurate three-dimensional (3D) measurement for large field of view (FOV) is currently a significant research field. Accordingly, system calibration is crucial to ensure accuracy. However typical calibration methods often involve the use of large calibration objects, which is not only expensive but also difficult to achieve sufficient accuracy. A novel method based on a dual-brand deep neural network (DNN) is proposed for the system calibration. Taking advantage of the concept of “divide and conquer”, the FOV is divided into sub-regions with a part of overlapping regions by a small calibration object, which forms a large calibration object covering the whole FOV. Then the sub-regions are fused into a global framework and further optimized by the proposed dual-brand DNN. The proposed method reduces the need for calibration objects while improving the calibration accuracy and generalization ability in large FOV. A series of experiments have been designed to prove the effectiveness and robustness of the proposed method.

中文翻译：

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一种基于深度学习的大范围场景高精度相机标定方法

大视场 (FOV) 的精确三维 (3D) 测量目前是一个重要的研究领域。因此，系统校准对于确保精度至关重要。然而，典型的校准方法通常涉及使用大型校准物体，这不仅昂贵而且难以达到足够的精度。提出了一种基于双品牌深度神经网络（DNN）的新方法用于系统校准。利用“分而治之”的理念，将一个小标定物将视场划分为部分重叠区域的子区域，形成覆盖整个视场的大标定物。然后，将子区域融合到全局框架中，并通过所提出的双品牌 DNN 进一步优化。该方法减少了对标定物体的需求，同时提高了大视场的标定精度和泛化能力。设计了一系列实验来证明所提出方法的有效性和鲁棒性。