# SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians

arXiv: https://arxiv.org/abs/2403.10427 · HTML: https://mlsanity.com/paper/2403.10427/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [Urban Radiance Fields](https://mlsanity.com/paper/2111.14643/) (2021)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction](https://mlsanity.com/paper/2402.17427/) (2024)
- [Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering](https://mlsanity.com/paper/2311.17089/) (2023)
- [Hallucinated Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2111.15246/) (2021)
- [NeRF for Outdoor Scene Relighting](https://mlsanity.com/paper/2112.05140/) (2021)
- [Cross-Ray Neural Radiance Fields for Novel-view Synthesis from Unconstrained Image Collections](https://mlsanity.com/paper/2307.08093/) (2023)
- [Pulsar: Efficient Sphere-based Neural Rendering](https://mlsanity.com/paper/2004.07484/) (2020)

## Built on by (most cited first, 30 of 61)

- [WildGaussians: 3D Gaussian Splatting in the Wild](https://mlsanity.com/paper/2407.08447/) (2024)
- [Recent Advances in 3D Gaussian Splatting](https://mlsanity.com/paper/2403.11134/) (2024)
- [3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2407.17418/) (2024)
- [SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting](https://mlsanity.com/paper/2406.20055/) (2024)
- [Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections](https://mlsanity.com/paper/2406.10373/) (2024)
- [WE-GS: An In-the-wild Efficient 3D Gaussian Representation for Unconstrained Photo Collections](https://mlsanity.com/paper/2406.02407/) (2024)
- [Splatfacto-W: A Nerfstudio Implementation of Gaussian Splatting for Unconstrained Photo Collections](https://mlsanity.com/paper/2407.12306/) (2024)
- [Luminance-GS: Adapting 3D Gaussian Splatting to Challenging Lighting Conditions with View-Adaptive Curve Adjustment](https://mlsanity.com/paper/2504.01503/) (2025)
- [LumiGauss: Relightable Gaussian Splatting in the Wild](https://mlsanity.com/paper/2408.04474/) (2024)
- [Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery](https://mlsanity.com/paper/2510.15869/) (2025)
- [HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting](https://mlsanity.com/paper/2412.03844/) (2024)
- [DeSplat: Decomposed Gaussian Splatting for Distractor-Free Rendering](https://mlsanity.com/paper/2411.19756/) (2024)
- [Dense-SfM: Structure from Motion with Dense Consistent Matching](https://mlsanity.com/paper/2501.14277/) (2025)
- [T-3DGS: Removing Transient Objects for 3D Scene Reconstruction](https://mlsanity.com/paper/2412.00155/) (2024)
- [3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration](https://mlsanity.com/paper/2403.11577/) (2024)
- [RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS](https://mlsanity.com/paper/2506.02751/) (2025)
- [GaRe: Relightable 3D Gaussian Splatting for Outdoor Scenes from Unconstrained Photo Collections](https://mlsanity.com/paper/2507.20512/) (2025)
- [Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images](https://mlsanity.com/paper/2501.14231/) (2025)
- [Aquatic-GS: A Hybrid 3D Representation for Underwater Scenes](https://mlsanity.com/paper/2411.00239/) (2024)
- [Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction](https://mlsanity.com/paper/2507.23006/) (2025)
- [Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis](https://mlsanity.com/paper/2602.18322/) (2026)
- [NexusSplats: Efficient 3D Gaussian Splatting in the Wild](https://mlsanity.com/paper/2411.14514/) (2024)
- [Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting](https://mlsanity.com/paper/2501.10788/) (2025)
- [Digital Twin Generation from Visual Data: A Survey](https://mlsanity.com/paper/2504.13159/) (2025)
- [ForestSplats: Deformable transient field for Gaussian Splatting in the Wild](https://mlsanity.com/paper/2503.06179/) (2025)
- [Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian Splatting](https://mlsanity.com/paper/2506.03538/) (2025)
- [SimVS: Simulating World Inconsistencies for Robust View Synthesis](https://mlsanity.com/paper/2412.07696/) (2024)
- [3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics Based Appearance-Medium Decoupling](https://mlsanity.com/paper/2505.21238/) (2025)
- [Adversarial Exploitation of Data Diversity Improves Visual Localization](https://mlsanity.com/paper/2412.00138/) (2024)
- [CHROMA: Consistent Harmonization of Multi-View Appearance via Bilateral Grid Prediction](https://mlsanity.com/paper/2507.15748/) (2025)

## Abstract (arXiv)

Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large computational cost of volumetric rendering. More recently, 3D Gaussian Splatting emerged as a much faster alternative with superior rendering quality and training efficiency, especially for small-scale and object-centric scenarios. Nevertheless, this technique suffers from poor performance on unstructured in-the-wild data. To tackle this, we extend over 3D Gaussian Splatting to handle unstructured image collections. We achieve this by modeling appearance to seize photometric variations in the rendered images. Additionally, we introduce a new mechanism to train transient Gaussians to handle the presence of scene occluders in an unsupervised manner. Experiments on diverse photo collection scenes and multi-pass acquisition of outdoor landmarks show the effectiveness of our method over prior works achieving state-of-the-art results with improved efficiency.