论文标题
视图一致的4D光场深度估计
View-consistent 4D Light Field Depth Estimation
论文作者
论文摘要
我们提出了一种方法，以一致的方式计算光场中每个子孔径图像的深度图。先前的光场深度估计方法通常仅估算中央子孔径视图的深度图，并与视图一致的估计斗争。我们的方法精确地定义了深度边缘，然后我们在中央视图中空间扩散了这些边缘。然后将这些深度估计以遮挡感知的方式传播到所有其他观点。最后，通过EPI空间的扩散完成了分离的区域。我们的方法相对于其他基于经典和深度学习的方法有效地运行，并在合成和现实世界的光场上实现了具有竞争力的定量指标和定性性能
We propose a method to compute depth maps for every sub-aperture image in a light field in a view consistent way. Previous light field depth estimation methods typically estimate a depth map only for the central sub-aperture view, and struggle with view consistent estimation. Our method precisely defines depth edges via EPIs, then we diffuse these edges spatially within the central view. These depth estimates are then propagated to all other views in an occlusion-aware way. Finally, disoccluded regions are completed by diffusion in EPI space. Our method runs efficiently with respect to both other classical and deep learning-based approaches, and achieves competitive quantitative metrics and qualitative performance on both synthetic and real-world light fields
