We present ZeroRF, a novel per-scene optimization method addressing the challenge of sparse view 360{\deg} reconstruction in neural field representations. Current breakthroughs like Neural Radiance Fields (NeRF) have demonstrated high-fidelity image synthesis but struggle with sparse input views. Existing methods, such as Generalizable NeRFs and per-scene optimization approaches, face limitations in data dependency, computational cost, and generalization across diverse scenarios. To overcome these challenges, we propose ZeroRF, whose key idea is to integrate a tailored Deep Image Prior into a factorized NeRF representation. Unlike traditional methods, ZeroRF parametrizes feature grids with a neural network generator, enabling efficient sparse view 360{\deg} reconstruction without any pretraining or additional regularization. Extensive experiments showcase ZeroRF's versatility and superiority in terms of both quality and speed, achieving state-of-the-art results on benchmark datasets. ZeroRF's significance extends to applications in 3D content generation and editing. Project page: https://sarahweiii.github.io/zerorf/

翻译：我们提出ZeroRF，一种新颖的逐场景优化方法，旨在解决神经场表示中稀疏视图360°重建的挑战。当前如神经辐射场（NeRF）等突破性技术虽能实现高保真图像合成，但在稀疏输入视图下表现不佳。现有方法，包括可泛化NeRF和逐场景优化方法，在数据依赖性、计算成本及跨场景泛化能力方面存在局限。为克服这些挑战，我们提出ZeroRF，其核心思想是将定制的深度图像先验（Deep Image Prior）融入分解式NeRF表示中。与传统方法不同，ZeroRF利用神经网络生成器参数化特征网格，无需任何预训练或额外正则化即可实现高效的稀疏视图360°重建。大量实验表明，ZeroRF在质量和速度上均具有显著优势，在基准数据集上达到了最先进水平。ZeroRF在3D内容生成与编辑等应用中具有重要价值。项目页面：https://sarahweiii.github.io/zerorf/