论文标题
SPARF：从几乎没有输入图像的3D稀疏辐射场的大规模学习
SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input Images
论文作者
论文摘要
神经辐射场（NERFS）的最新进展将新型视图合成问题视为稀疏的辐射场（SRF）优化，使用稀疏体素优化，以提高效率和快速渲染（plenoxels，InstantNGP）。为了利用机器学习和采用SRF作为3D表示形式，我们提出了Sparf，Sparf是一种基于大规模的Shapenet合成数据集，用于新型视图合成，由$ \ sim $ \ sim由1700万美元的图像组成，由近40,000个形状呈现为高分辨率（400 x 400 x 400 pixels）。该数据集的数量级比现有的合成数据集大，以用于新的视图合成，并且包含超过一百万个具有多个素分辨率的3D优化的辐射场。此外，我们提出了一条新型管道（Surfnet），该管道学会了仅从几个视图中产生稀疏的体素辐射场。这是通过使用密集收集的SPARF数据集和3D稀疏卷积来完成的。 Surfnet采用了几个/一个图像和专门的SRF损失的部分SRF来学习产生高质量的稀疏体素辐射场，可以从新颖的视图中呈现。与最近的基线相比，我们的方法实现了最新的最新方法，从而导致了不受限制的新型视图合成的任务。 SPARF数据集在项目网站https://abdullahamdi.com/sparf/上公开使用代码和模型。
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 is made public with the code and models on the project website https://abdullahamdi.com/sparf/ .
