Recent advances in Neural Radiance Fields (NeRFs) treat the problem of novel view synthesis as Sparse Radiance Field (SRF) optimization using sparse voxels for efficient and fast rendering (plenoxels,InstantNGP). In order to leverage machine learning and adoption of SRFs as a 3D representation, we present SPARF, a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of $\sim$ 17 million images rendered from nearly 40,000 shapes at high resolution (400 X 400 pixels). The dataset is orders of magnitude larger than existing synthetic datasets for novel view synthesis and includes more than one million 3D-optimized radiance fields with multiple voxel resolutions. Furthermore, we propose a novel pipeline (SuRFNet) that learns to generate sparse voxel radiance fields from only few views. This is done by using the densely collected SPARF dataset and 3D sparse convolutions. SuRFNet employs partial SRFs from few/one images and a specialized SRF loss to learn to generate high-quality sparse voxel radiance fields that can be rendered from novel views. Our approach achieves state-of-the-art results in the task of unconstrained novel view synthesis based on few views on ShapeNet as compared to recent baselines. The SPARF dataset will be made public with the code and models on the project website https://abdullahamdi.com/sparf/ .
翻译:神经辐射场( NeRFs) 的最新进展将新观点合成问题视为使用稀有的蒸馏液优化( Plennoxels, InstantNGP) 使用稀有的蒸馏液优化(SRF) 。为了利用机器学习和采用战略成果框架作为3D代表,我们提出了SPARF,这是一个大型的以形状网络为基础的合成合成数据集,用于新视觉合成,由高分辨率(400x400像素)近40,000个形状提供的1,700万美元图像组成。数据集是比现有合成数据集更大的数量级级,用于新颖视图合成,包括超过100万个3D优化的光亮场,并有多个 voxel 决议。此外,我们提出了一个新的管道(SuRFNet),该管道只从少数观点中学会生成稀有的蒸馏素光场。这是通过使用密集收集的SPRAF数据集和3D 稀有的图像。 SuRFNet 将部分SRF 用于少数/one图像和专门的SRF损失,以便学习以高品质稀有的稀有的稀有版本版本版本版本版本版本版本版本网站/图像,在新版本网站上,可以实现我们的新版本的版本的版本的版本版本的版本化的版本的版本。</s>