# SkySplat: Generalizable 3D Gaussian Splatting from Multi-Temporal Sparse Satellite Images

arXiv: https://arxiv.org/abs/2508.09479 · HTML: https://mlsanity.com/paper/2508.09479/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 13 Aug 2025.

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

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction](https://mlsanity.com/paper/2312.12337/) (2023)
- [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization](https://mlsanity.com/paper/2303.07418/) (2023)
- [DepthSplat: Connecting Gaussian Splatting and Depth](https://mlsanity.com/paper/2410.13862/) (2024)
- [SPARF: Neural Radiance Fields from Sparse and Noisy Poses](https://mlsanity.com/paper/2211.11738/) (2022)
- [WildGaussians: 3D Gaussian Splatting in the Wild](https://mlsanity.com/paper/2407.08447/) (2024)
- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Transformers](https://mlsanity.com/paper/2408.13770/) (2024)
- [Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras](https://mlsanity.com/paper/2203.08896/) (2022)
- [HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction](https://mlsanity.com/paper/2410.06245/) (2024)
- [Semantic Stereo for Incidental Satellite Images](https://mlsanity.com/paper/1811.08739/) (2018)
- [Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry](https://mlsanity.com/paper/2104.09877/) (2021)
- [Leveraging Vision Reconstruction Pipelines for Satellite Imagery](https://mlsanity.com/paper/1910.02989/) (2019)
- [Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching](https://mlsanity.com/paper/2109.11121/) (2021)
- [RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS](https://mlsanity.com/paper/2506.02751/) (2025)
- [SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition](https://mlsanity.com/paper/2312.16215/) (2023)
- [Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images with Depth and Normal Supervision](https://mlsanity.com/paper/2502.08352/) (2025)

## Built on by (most cited first, 14 of 14)

- [Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery](https://mlsanity.com/paper/2510.15869/) (2025)
- [From Orbit to Ground: Generative City Photogrammetry from Extreme Off-Nadir Satellite Images](https://mlsanity.com/paper/2512.07527/) (2025)
- [EOGS++: Earth Observation Gaussian Splatting with Internal Camera Refinement and Direct Panchromatic Rendering](https://mlsanity.com/paper/2511.16542/) (2025)
- [ShadowGS: Shadow-Aware 3D Gaussian Splatting for Satellite Imagery](https://mlsanity.com/paper/2601.00939/) (2026)
- [SatSurfGS: Generalizable 2D Gaussian Splatting for Sparse-View Satellite Surface Reconstruction](https://mlsanity.com/paper/2605.07181/) (2026)
- [EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction](https://mlsanity.com/paper/2607.00417/) (2026)
- [SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery](https://mlsanity.com/paper/2603.18634/) (2026)
- [DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis](https://mlsanity.com/paper/2604.13416/) (2026)
- [Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images](https://mlsanity.com/paper/2605.19656/) (2026)
- [SatSplatDiff: Geometry-preserving generative refinement for high-fidelity satellite Gaussian Splatting](https://mlsanity.com/paper/2606.27223/) (2026)
- [Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction](https://mlsanity.com/paper/2607.16286/) (2026)
- [GeoRay: Gauge-Aware Feed-Forward Satellite 3D Reconstruction in the Geodetic Frame](https://mlsanity.com/paper/2608.29680/) (2026)
- [Robust 3D Reconstruction from Multi-View Optical Satellite Imagery via Reliability-Aware Height-Evidence Fusion in Gaussian Splatting](https://mlsanity.com/paper/2609.16772/) (2026)
- [Agentic Building-Aware Satellite Gaussian Splatting for Auditable Urban DSM Reconstruction](https://mlsanity.com/paper/2609.25578/) (2026)

## Abstract (arXiv)

Three-dimensional scene reconstruction from sparse-view satellite images is a long-standing and challenging task. While 3D Gaussian Splatting (3DGS) and its variants have recently attracted attention for its high efficiency, existing methods remain unsuitable for satellite images due to incompatibility with rational polynomial coefficient (RPC) models and limited generalization capability. Recent advances in generalizable 3DGS approaches show potential, but they perform poorly on multi-temporal sparse satellite images due to limited geometric constraints, transient objects, and radiometric inconsistencies. To address these limitations, we propose SkySplat, a novel self-supervised framework that integrates the RPC model into the generalizable 3DGS pipeline, enabling more effective use of sparse geometric cues for improved reconstruction. SkySplat relies only on RGB images and radiometric-robust relative height supervision, thereby eliminating the need for ground-truth height maps. Key components include a Cross-Self Consistency Module (CSCM), which mitigates transient object interference via consistency-based masking, and a multi-view consistency aggregation strategy that refines reconstruction results. Compared to per-scene optimization methods, SkySplat achieves an 86 times speedup over EOGS with higher accuracy. It also outperforms generalizable 3DGS baselines, reducing MAE from 13.18 m to 1.80 m on the DFC19 dataset significantly, and demonstrates strong cross-dataset generalization on the MVS3D benchmark. The is available at https://github.com/NanCheng2001/SkySplat-main