Novel view synthesis is a long-standing problem in machine learning and computer vision. Significant progress has recently been made in developing neural scene representations and rendering techniques that synthesize photorealistic images from arbitrary views. These representations, however, are extremely slow to train and often also slow to render. Inspired by neural variants of image-based rendering, we develop a new neural rendering approach with the goal of quickly learning a high-quality representation which can also be rendered in real-time. Our approach, MetaNLR++, accomplishes this by using a unique combination of a neural shape representation and 2D CNN-based image feature extraction, aggregation, and re-projection. To push representation convergence times down to minutes, we leverage meta learning to learn neural shape and image feature priors which accelerate training. The optimized shape and image features can then be extracted using traditional graphics techniques and rendered in real time. We show that MetaNLR++ achieves similar or better novel view synthesis results in a fraction of the time that competing methods require.

中文翻译：

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使用元学习快速训练神经 Lumigraph 表示

新颖的视图合成是机器学习和计算机视觉中的一个长期存在的问题。最近在开发从任意视图合成逼真图像的神经场景表示和渲染技术方面取得了重大进展。然而，这些表示的训练速度非常慢，而且渲染速度通常也很慢。受基于图像渲染的神经变体的启发，我们开发了一种新的神经渲染方法，目标是快速学习也可以实时渲染的高质量表示。我们的方法 MetaNLR++ 通过使用神经形状表示和基于 2D CNN 的图像特征提取、聚合和重新投影的独特组合来实现这一点。要将表示收敛时间缩短到几分钟，我们利用元学习来学习加速训练的神经形状和图像特征先验。然后可以使用传统图形技术提取优化的形状和图像特征并实时渲染。我们展示了 MetaNLR++ 在竞争方法所需的一小部分时间内实现了类似或更好的新颖视图合成结果。