论文标题
Scannerf：神经辐射场的可扩展基准
ScanNeRF: a Scalable Benchmark for Neural Radiance Fields
论文作者
论文摘要
在本文中，我们提出了有史以来第一个用于评估神经辐射场（NERFS）的真实基准思想，通常是神经渲染（NR）框架。我们设计并实施了有效的管道，以毫不费力地扫描实际对象。我们的扫描站的建造量不到500美元，可以在5分钟内收集大约4000张扫描物体的图像。这样的平台用于构建Scannerf，该数据集的特征是几种火车/Val/测试拆分，旨在基准在不同条件下进行现代NERF方法的性能。因此，我们在其上评估了三个尖端的NERF变体，以突出它们的优势和劣势。该数据集可在我们的项目页面上提供，并提供在线基准测试，以促进越来越多的NERF的开发。
In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and implement an effective pipeline for scanning real objects in quantity and effortlessly. Our scan station is built with less than 500$ hardware budget and can collect roughly 4000 images of a scanned object in just 5 minutes. Such a platform is used to build ScanNeRF, a dataset characterized by several train/val/test splits aimed at benchmarking the performance of modern NeRF methods under different conditions. Accordingly, we evaluate three cutting-edge NeRF variants on it to highlight their strengths and weaknesses. The dataset is available on our project page, together with an online benchmark to foster the development of better and better NeRFs.
