# S2RF: Semantically Stylized Radiance Fields

arXiv: https://arxiv.org/abs/2309.01252 · HTML: https://mlsanity.com/paper/2309.01252/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 22 of 22)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures](https://mlsanity.com/paper/2208.00277/) (2022)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [ARF: Artistic Radiance Fields](https://mlsanity.com/paper/2206.06360/) (2022)
- [SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields](https://mlsanity.com/paper/2211.12254/) (2022)
- [StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning](https://mlsanity.com/paper/2205.12183/) (2022)
- [EfficientNeRF: Efficient Neural Radiance Fields](https://mlsanity.com/paper/2206.00878/) (2022)
- [Stylizing 3D Scene via Implicit Representation and HyperNetwork](https://mlsanity.com/paper/2105.13016/) (2021)
- [SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field](https://mlsanity.com/paper/2303.13277/) (2023)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [Learning to Stylize Novel Views](https://mlsanity.com/paper/2105.13509/) (2021)
- [3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations](https://mlsanity.com/paper/2108.12958/) (2021)
- [StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions](https://mlsanity.com/paper/2112.01530/) (2021)
- [3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image](https://mlsanity.com/paper/2112.00169/) (2021)
- [Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization](https://mlsanity.com/paper/2212.02766/) (2022)
- [4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions](https://mlsanity.com/paper/2212.04701/) (2022)

## Built on by (most cited first, 3 of 3)

- [StylizedGS: Controllable Stylization for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.05220/) (2024)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [ICE-G: Image Conditional Editing of 3D Gaussian Splats](https://mlsanity.com/paper/2406.08488/) (2024)

## Abstract (arXiv)

We present our method for transferring style from any arbitrary image(s) to object(s) within a 3D scene. Our primary objective is to offer more control in 3D scene stylization, facilitating the creation of customizable and stylized scene images from arbitrary viewpoints. To achieve this, we propose a novel approach that incorporates nearest neighborhood-based loss, allowing for flexible 3D scene reconstruction while effectively capturing intricate style details and ensuring multi-view consistency.