论文标题
FWD：带有前向扭曲和深度的实时小说视图综合
FWD: Real-time Novel View Synthesis with Forward Warping and Depth
论文作者
论文摘要
新型视图综合（NVS）是一项具有挑战性的任务，需要从新观点来生成场景的影像图像，在新观点中，质量和速度对应用都很重要。以前的基于图像的渲染（IBR）方法很快，但是当输入视图稀疏时质量较差。最近的神经辐射场（NERF）和可概括的变体给出了令人印象深刻的结果，但不是实时的。在我们的论文中，我们提出了一种具有稀疏输入的可推广的NVS方法，称为FWD，该方法可实时提供高质量的合成。凭借明确的深度和可区分的渲染，它以130-1000 x的加速和更好的感知质量取得了SOTA方法的竞争结果。如果有的话，我们可以在训练或推理过程中无缝整合传感器的深度，以提高图像质量，同时保持实时速度。随着深度传感器的越来越多的流行率，我们希望使用深度的方法将变得越来越有用。
Novel view synthesis (NVS) is a challenging task requiring systems to generate photorealistic images of scenes from new viewpoints, where both quality and speed are important for applications. Previous image-based rendering (IBR) methods are fast, but have poor quality when input views are sparse. Recent Neural Radiance Fields (NeRF) and generalizable variants give impressive results but are not real-time. In our paper, we propose a generalizable NVS method with sparse inputs, called FWD, which gives high-quality synthesis in real-time. With explicit depth and differentiable rendering, it achieves competitive results to the SOTA methods with 130-1000x speedup and better perceptual quality. If available, we can seamlessly integrate sensor depth during either training or inference to improve image quality while retaining real-time speed. With the growing prevalence of depths sensors, we hope that methods making use of depth will become increasingly useful.
