# ZeroRF: Fast Sparse View 360deg Reconstruction with Zero Pretraining

arXiv: https://arxiv.org/abs/2312.09249 · HTML: https://mlsanity.com/paper/2312.09249/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 69 of its 78 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 30 of 69)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction](https://mlsanity.com/paper/2309.13101/) (2023)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction](https://mlsanity.com/paper/2104.10078/) (2021)
- [RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs](https://mlsanity.com/paper/2112.00724/) (2021)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)

## Built on by (most cited first, 24 of 24)

- [CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model](https://mlsanity.com/paper/2403.05034/) (2024)
- [MeshLRM: Large Reconstruction Model for High-Quality Meshes](https://mlsanity.com/paper/2404.12385/) (2024)
- [GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting](https://mlsanity.com/paper/2402.10259/) (2024)
- [SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction](https://mlsanity.com/paper/2409.11211/) (2024)
- [LRM-Zero: Training Large Reconstruction Models with Synthesized Data](https://mlsanity.com/paper/2406.09371/) (2024)
- [SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views](https://mlsanity.com/paper/2408.10195/) (2024)
- [FrugalNeRF: Fast Convergence for Extreme Few-shot Novel View Synthesis without Learned Priors](https://mlsanity.com/paper/2410.16271/) (2024)
- [SplatFormer: Point Transformer for Robust 3D Gaussian Splatting](https://mlsanity.com/paper/2411.06390/) (2024)
- [LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields](https://mlsanity.com/paper/2504.20026/) (2025)
- [FlexiDreamer: Single Image-to-3D Generation with FlexiCubes](https://mlsanity.com/paper/2404.00987/) (2024)
- [Factorized Motion Fields for Fast Sparse Input Dynamic View Synthesis](https://mlsanity.com/paper/2404.11669/) (2024)
- [AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark](https://mlsanity.com/paper/2409.15041/) (2024)
- [AugGS: Self-augmented Gaussians with Structural Masks for Sparse-view 3D Reconstruction](https://mlsanity.com/paper/2408.04831/) (2024)
- [Sparse-View 3D Reconstruction: Recent Advances and Open Challenges](https://mlsanity.com/paper/2507.16406/) (2025)
- [4C4D: 4 Camera 4D Gaussian Splatting](https://mlsanity.com/paper/2604.04063/) (2026)
- [FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis](https://mlsanity.com/paper/2402.14586/) (2024)
- [Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions](https://mlsanity.com/paper/2404.19015/) (2024)
- [FourieRF: Few-Shot NeRFs via Progressive Fourier Frequency Control](https://mlsanity.com/paper/2502.01405/) (2025)
- [A View-consistent Sampling Method for Regularized Training of Neural Radiance Fields](https://mlsanity.com/paper/2507.04408/) (2025)
- [PS-GS: Gaussian Splatting for Multi-View Photometric Stereo](https://mlsanity.com/paper/2507.18231/) (2025)
- [ISExplore:Informative Segment Selection for Efficient Personalized 3D Talking Face Generation](https://mlsanity.com/paper/2511.07940/) (2025)
- [NeuralMeshing: Complete Object Mesh Extraction from Casual Captures](https://mlsanity.com/paper/2508.16026/) (2025)
- [HICT: High-precision 3D CBCT reconstruction from a single X-ray](https://mlsanity.com/paper/2604.00792/) (2026)
- [VisDom: Sparse Novel View Synthesis with Visible Domain Constraint](https://mlsanity.com/paper/2606.20531/) (2026)

## Abstract (arXiv)

We present ZeroRF, a novel per-scene optimization method addressing the challenge of sparse view 360deg 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 360deg 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/