# GP-GS: Gaussian Processes Densification for 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2502.02283 · HTML: https://mlsanity.com/paper/2502.02283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 4 Feb 2025.

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

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [DepthSplat: Connecting Gaussian Splatting and Depth](https://mlsanity.com/paper/2410.13862/) (2024)
- [GaussianPro: 3D Gaussian Splatting with Progressive Propagation](https://mlsanity.com/paper/2402.14650/) (2024)
- [Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images](https://mlsanity.com/paper/2311.13398/) (2023)
- [Revising Densification in Gaussian Splatting](https://mlsanity.com/paper/2404.06109/) (2024)
- [RenderWorld: World Model with Self-Supervised 3D Label](https://mlsanity.com/paper/2409.11356/) (2024)
- [3DSceneEditor: Controllable 3D Scene Editing with Gaussian Splatting](https://mlsanity.com/paper/2412.01583/) (2024)
- [WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting](https://mlsanity.com/paper/2412.18862/) (2024)
- [Robot Shape and Location Retention in Video Generation Using Diffusion Models](https://mlsanity.com/paper/2407.02873/) (2024)

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

- [ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation](https://mlsanity.com/paper/2508.04153/) (2025)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) enables photorealistic rendering but suffers from artefacts due to sparse Structure-from-Motion (SfM) initialisation. To address this limitation, we propose GP-GS, a Gaussian Process (GP) based densification framework for 3DGS optimisation. GP-GS formulates point cloud densification as a continuous regression problem, where a GP learns a local mapping from 2D pixel coordinates to 3D position and colour attributes. An adaptive neighbourhood-based sampling strategy generates candidate pixels for inference, while GP-predicted uncertainty is used to filter unreliable predictions, reducing noise and preserving geometric structure. Extensive experiments on synthetic and real-world benchmarks demonstrate that GP-GS consistently improves reconstruction quality and rendering fidelity, achieving up to 1.12 dB PSNR improvement over strong baselines.