# FrontierGS: Progressive View-Space Frontier Expansion for Sparse 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2511.16030 · HTML: https://mlsanity.com/paper/2511.16030/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 50 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 30 of 33)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction](https://mlsanity.com/paper/2312.12337/) (2023)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs](https://mlsanity.com/paper/2112.00724/) (2021)
- [Splatter Image: Ultra-Fast Single-View 3D Reconstruction](https://mlsanity.com/paper/2312.13150/) (2023)
- [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization](https://mlsanity.com/paper/2303.07418/) (2023)
- [GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation](https://mlsanity.com/paper/2403.14621/) (2024)
- [FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting](https://mlsanity.com/paper/2312.00451/) (2023)
- [DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization](https://mlsanity.com/paper/2403.06912/) (2024)
- [Taming 3DGS: High-Quality Radiance Fields with Limited Resources](https://mlsanity.com/paper/2406.15643/) (2024)
- [3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting](https://mlsanity.com/paper/2312.09228/) (2023)
- [CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians](https://mlsanity.com/paper/2404.01133/) (2024)
- [SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views](https://mlsanity.com/paper/2206.05737/) (2022)
- [SparseGS: Sparse View Synthesis using 3D Gaussian Splatting](https://mlsanity.com/paper/2312.00206/) (2023)
- [CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization](https://mlsanity.com/paper/2405.12110/) (2024)
- [Structure-Aware Sparse-View X-ray 3D Reconstruction](https://mlsanity.com/paper/2311.10959/) (2023)
- [View-Consistent 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2403.11868/) (2024)
- [DropGaussian: Structural Regularization for Sparse-view Gaussian Splatting](https://mlsanity.com/paper/2504.00773/) (2025)
- [CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians](https://mlsanity.com/paper/2403.19495/) (2024)
- [ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields](https://mlsanity.com/paper/2305.00041/) (2023)
- [Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis](https://mlsanity.com/paper/2410.18822/) (2024)
- [Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment](https://mlsanity.com/paper/2403.11176/) (2024)
- [LoopSparseGS: Loop Based Sparse-View Friendly Gaussian Splatting](https://mlsanity.com/paper/2408.00254/) (2024)

## Built on by (most cited first, 1 of 1)

- [LiDAR-EVS: Enhance Extrapolated View Synthesis for 3D Gaussian Splatting with Pseudo-LiDAR Supervision](https://mlsanity.com/paper/2603.14763/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) enables efficient, high-fidelity novel view synthesis, yet its performance degrades severely under sparse-view supervision due to extreme ill-posedness. While existing approaches introduce various geometric priors or heuristic regularizations to mitigate overfitting, they largely overlook the underlying optimization dynamics. In this paper, we identify a distinct training pathology termed view-space generalization collapse: as optimization proceeds, observed-view fitting error continuously decreases, whereas rendering quality at unseen viewpoints initially peaks at an intermediate stage and subsequently deteriorates. This transient peak indicates that early-stage optimization implicitly uncovers valid local 3D structures before overfitting along training rays destroys them. Grounded in this finding, we propose FrontierGS, a progressive view-space expansion framework designed to capture and anchor these transiently reliable states. Operating along an expanding generalization frontier, FrontierGS systematically probes candidate viewpoints around observed cameras, verifies their geometric and perceptual consistency, and progressively incorporates reliable candidates into the supervision set to permanently preserve valid geometry before collapse occurs. Extensive experiments across the LLFF, MipNeRF-360, and DTU benchmarks demonstrate that FrontierGS effectively eliminates view-space collapse, consistently outperforming state-of-the-art baselines in both rendering fidelity and geometric accuracy. Project page: https://github.com/Zijian1026/FrontierGS