# AD-GS: Alternating Densification for Sparse-Input 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2509.11003 · HTML: https://mlsanity.com/paper/2509.11003/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 13 Sep 2025.

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

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [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)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization](https://mlsanity.com/paper/2303.07418/) (2023)
- [FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting](https://mlsanity.com/paper/2312.00451/) (2023)
- [No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images](https://mlsanity.com/paper/2410.24207/) (2024)
- [Stereo Magnification: Learning View Synthesis using Multiplane Images](https://mlsanity.com/paper/1805.09817/) (2018)
- [DeepStereo: Learning to Predict New Views from the World's Imagery](https://mlsanity.com/paper/1506.06825/) (2015)
- [FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views](https://mlsanity.com/paper/2502.12138/) (2025)
- [CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization](https://mlsanity.com/paper/2405.12110/) (2024)
- [InstantSplat: Sparse-view Gaussian Splatting in Seconds](https://mlsanity.com/paper/2403.20309/) (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)
- [SimpleNeRF: Regularizing Sparse Input Neural Radiance Fields with Simpler Solutions](https://mlsanity.com/paper/2309.03955/) (2023)
- [ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields](https://mlsanity.com/paper/2305.00041/) (2023)
- [A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose](https://mlsanity.com/paper/2405.03659/) (2024)
- [Improving Adaptive Density Control for 3D Gaussian Splatting](https://mlsanity.com/paper/2503.14274/) (2025)

## Built on by (most cited first, 8 of 8)

- [GeoQuery: Geometry-Query Diffusion for Sparse-View Reconstruction](https://mlsanity.com/paper/2605.12399/) (2026)
- [GaussVid: Sparse-View Gaussian Splatting with 3D-Aware Video Diffusion Priors](https://mlsanity.com/paper/2608.21849/) (2026)
- [EntON: Eigenentropy-Optimized Neighborhood Densification in 3D Gaussian Splatting](https://mlsanity.com/paper/2603.06216/) (2026)
- [PanoPlane: Plane-Aware Panoramic Completion for Sparse-View Indoor 3D Gaussian Splatting](https://mlsanity.com/paper/2605.14135/) (2026)
- [Temporally Aware Densification for Dynamic 3D Gaussian Splatting](https://mlsanity.com/paper/2606.23212/) (2026)
- [L2D2-GS: Learning to Densify for Feedforward Dynamic Gaussian Scene Reconstruction](https://mlsanity.com/paper/2606.29374/) (2026)
- [Improving Sparse-View 3DGS Generalization via Flat Minima Optimization](https://mlsanity.com/paper/2607.00885/) (2026)
- [TruncGradGS: Improved 3D Gaussian Splatting via Truncated Gradient Updates](https://mlsanity.com/paper/2609.03534/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has shown impressive results in real-time novel view synthesis. However, it often struggles under sparse-view settings, producing undesirable artifacts such as floaters, inaccurate geometry, and overfitting due to limited observations. We find that a key contributing factor is uncontrolled densification, where adding Gaussian primitives rapidly without guidance can harm geometry and cause artifacts. We propose AD-GS, a novel alternating densification framework that interleaves high and low densification phases. During high densification, the model densifies aggressively, followed by photometric loss based training to capture fine-grained scene details. Low densification then primarily involves aggressive opacity pruning of Gaussians followed by regularizing their geometry through pseudo-view consistency and edge-aware depth smoothness. This alternating approach helps reduce overfitting by carefully controlling model capacity growth while progressively refining the scene representation. Extensive experiments on challenging datasets demonstrate that AD-GS significantly improves rendering quality and geometric consistency compared to existing methods. The source code for our model can be found on our project page: https://gurutvapatle.github.io/publications/2025/ADGS.html .