标题： 一个新的数据集和多相机帧合成的比较 显示英文标题

标题： A new dataset and comparison for multi-camera frame synthesis

摘要： 存在许多用于图像序列中帧合成的方法，但可以大致分为帧插值和视角合成技术。 从根本上说，帧插值和视角合成都解决相同的任务，即在时间或空间上给定周围帧的情况下进行帧插值。 然而，大多数帧插值数据集侧重于时间方面，使用单个摄像头在时间和空间中移动，而视角合成数据集通常偏向于立体深度估计的应用场景。 这使得直接比较视角合成和帧插值方法变得具有挑战性。 在本文中，我们开发了一个新颖的多摄像机数据集，使用自定义构建的密集线性摄像机阵列，以实现这些方法之间的公平比较。 我们将经典方法和深度学习帧插值器与一种视角合成方法（3D高斯点云）在视角中间处理任务中进行评估。 我们的结果表明，在真实图像数据上，深度学习方法并未显著优于经典方法，3D高斯点云的实际表现甚至比帧插值器低多达3.5 dB PSNR。 然而，在合成场景中，情况则相反——在95%置信度下，3D高斯点云比帧插值算法高出近5 dB PSNR。 显示英文摘要

摘要： Many methods exist for frame synthesis in image sequences but can be broadly categorised into frame interpolation and view synthesis techniques. Fundamentally, both frame interpolation and view synthesis tackle the same task, interpolating a frame given surrounding frames in time or space. However, most frame interpolation datasets focus on temporal aspects with single cameras moving through time and space, while view synthesis datasets are typically biased toward stereoscopic depth estimation use cases. This makes direct comparison between view synthesis and frame interpolation methods challenging. In this paper, we develop a novel multi-camera dataset using a custom-built dense linear camera array to enable fair comparison between these approaches. We evaluate classical and deep learning frame interpolators against a view synthesis method (3D Gaussian Splatting) for the task of view in-betweening. Our results reveal that deep learning methods do not significantly outperform classical methods on real image data, with 3D Gaussian Splatting actually underperforming frame interpolators by as much as 3.5 dB PSNR. However, in synthetic scenes, the situation reverses -- 3D Gaussian Splatting outperforms frame interpolation algorithms by almost 5 dB PSNR at a 95% confidence level.