论文标题
使用双束图上的全向图像上的易耐失形单眼深度估计
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap
论文作者
论文摘要
估计全向图像的深度比正常视野（NFOV）图像的深度更具挑战性，因为不同的失真可以显着扭曲对象的形状。现有方法在估计全向图像深度的同时遭受麻烦的失真，导致性能较低。为了减少失真影响的负面影响，我们提出了使用双束式的易耐失真的全向深度估计算法。它包括两个模块：双束深度估计（DCDE）模块和边界修订（BR）模块。在DCDE模块中，我们提出了一个基于旋转的双束模型，以估计准确的NFOV深度，从而在全向深度上以边界不连续性的成本减少了失真。然后，设计了一个边界修订模块，以平滑不连续的边界，这有助于精确和视觉上连续的全向深度。广泛的实验证明了我们方法比其他最先进的解决方案的优越性。
Estimating the depth of omnidirectional images is more challenging than that of normal field-of-view (NFoV) images because the varying distortion can significantly twist an object's shape. The existing methods suffer from troublesome distortion while estimating the depth of omnidirectional images, leading to inferior performance. To reduce the negative impact of the distortion influence, we propose a distortion-tolerant omnidirectional depth estimation algorithm using a dual-cubemap. It comprises two modules: Dual-Cubemap Depth Estimation (DCDE) module and Boundary Revision (BR) module. In DCDE module, we present a rotation-based dual-cubemap model to estimate the accurate NFoV depth, reducing the distortion at the cost of boundary discontinuity on omnidirectional depths. Then a boundary revision module is designed to smooth the discontinuous boundaries, which contributes to the precise and visually continuous omnidirectional depths. Extensive experiments demonstrate the superiority of our method over other state-of-the-art solutions.
