论文标题
光场视图通过孔径差异和扭曲置信度图
Light Field View Synthesis via Aperture Disparity and Warping Confidence Map
论文作者
论文摘要
本文提出了一种基于学习的方法，可以从任意相机位置综合视图，给定一组稀疏的图像。这种新型观点综合的一个关键挑战是由重建过程引起的，因为由于光路中的障碍物，来自不同输入图像的视图可能不一致。我们通过在设计卷积神经网络中共同建模对形的性质和遮挡来克服这一点。我们首先定义和计算孔径差异图，该图近似于视差，并测量两个视图之间的像素偏移。尽管这与自由空间渲染有关，并且可能会在对象边界附近失败，但我们进一步开发了一个扭曲的置信图，以解决这些挑战性区域中的像素遮挡。对所提出的方法进行了评估，并在各种现实世界和合成光场场景上进行评估，并且在几种最新技术方面显示出更好的性能。
This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images. A key challenge for this novel view synthesis arises from the reconstruction process, when the views from different input images may not be consistent due to obstruction in the light path. We overcome this by jointly modeling the epipolar property and occlusion in designing a convolutional neural network. We start by defining and computing the aperture disparity map, which approximates the parallax and measures the pixel-wise shift between two views. While this relates to free-space rendering and can fail near the object boundaries, we further develop a warping confidence map to address pixel occlusion in these challenging regions. The proposed method is evaluated on diverse real-world and synthetic light field scenes, and it shows better performance over several state-of-the-art techniques.
