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arXiv · 2312.09249

ZeroRF: Fast Sparse View 360{\deg} Reconstruction with Zero Pretraining

Ruoxi Shi·Xinyue Wei·Cheng Wang·Hao Su
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Abstract

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/

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Ruoxi Shi, Xinyue Wei, Cheng Wang, Hao Su. 2023-12-14. ZeroRF: Fast Sparse View 360{\deg} Reconstruction with Zero Pretraining. https://arxiv.org/abs/2312.09249

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