# Gaussian On-the-Fly Splatting: A Progressive Framework for Robust Near Real-Time 3DGS Optimization

arXiv: https://arxiv.org/abs/2503.13086 · HTML: https://mlsanity.com/paper/2503.13086/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 49 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 30 of 31)

- [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 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [Gaussian Splatting SLAM](https://mlsanity.com/paper/2312.06741/) (2023)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces](https://mlsanity.com/paper/2311.17977/) (2023)
- [Taming 3DGS: High-Quality Radiance Fields with Limited Resources](https://mlsanity.com/paper/2406.15643/) (2024)
- [GaussianPro: 3D Gaussian Splatting with Progressive Propagation](https://mlsanity.com/paper/2402.14650/) (2024)
- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [SparseGS: Sparse View Synthesis using 3D Gaussian Splatting](https://mlsanity.com/paper/2312.00206/) (2023)
- [Reducing the Memory Footprint of 3D Gaussian Splatting](https://mlsanity.com/paper/2406.17074/) (2024)
- [FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization](https://mlsanity.com/paper/2403.06908/) (2024)
- [3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2407.17418/) (2024)
- [DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus](https://mlsanity.com/paper/2405.13943/) (2024)
- [DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines](https://mlsanity.com/paper/2401.05345/) (2023)
- [AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius](https://mlsanity.com/paper/2409.08669/) (2024)
- [EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation](https://mlsanity.com/paper/2404.12777/) (2024)
- [GSFusion: Online RGB-D Mapping Where Gaussian Splatting Meets TSDF Fusion](https://mlsanity.com/paper/2408.12677/) (2024)
- [RetinaGS: Scalable Training for Dense Scene Rendering with Billion-Scale 3D Gaussians](https://mlsanity.com/paper/2406.11836/) (2024)
- [DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2411.17660/) (2024)
- [Identifying Unnecessary 3D Gaussians using Clustering for Fast Rendering of 3D Gaussian Splatting](https://mlsanity.com/paper/2402.13827/) (2024)
- [SSNeRF: Sparse View Semi-supervised Neural Radiance Fields with Augmentation](https://mlsanity.com/paper/2408.09144/) (2024)
- [On-the-Fly SfM: What you capture is What you get](https://mlsanity.com/paper/2309.11883/) (2023)

## Built on by (most cited first, 4 of 4)

- [On-the-fly Large-scale 3D Reconstruction from Multi-Camera Rigs](https://mlsanity.com/paper/2512.08498/) (2025)
- [Physics-Aware 3D Gaussian Editing for Driving Scene Generation](https://mlsanity.com/paper/2605.25373/) (2026)
- [A-TDOM: Active TDOM via On-the-Fly 3DGS](https://mlsanity.com/paper/2509.12759/) (2025)
- [OF3GS: On-the-Fly Feed-Forward 3D Gaussian Splatting from Unposed Images](https://mlsanity.com/paper/2606.03254/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) achieves high-fidelity rendering with fast real-time performance, but existing methods rely on offline training after full Structure-from-Motion (SfM) processing. In contrast, this work introduces Gaussian on-the-fly Splatting (abbreviated as On-the-Fly GS), a progressive framework enabling near real-time 3DGS optimization during image capture. As each image arrives, its pose and sparse points are updated via On-the-Fly SfM, and newly optimized Gaussians are immediately integrated into the 3DGS field. To achieve this, we propose a progressive Local & Semi-Global optimization to prioritize the new image and its neighbors by their corresponding overlapping relationship, allowing the new image and its overlapping images to get more training. To further stabilize training across previous and new images, an adaptive learning rate schedule balances the iterations and the learning rate. Extensive experiments on multiple benchmarks show that our On-the-Fly GS reduces training time significantly, optimizing each new image in seconds with minimal rendering loss, offering one of the first practical steps toward rapid, progressive 3DGS reconstruction.