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

📝 Original Info

• Title: AD-GS: Alternating Densification for Sparse-Input 3D Gaussian Splatting
• ArXiv ID: 2509.11003
• Date: 2025-09-13
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (보통 프로젝트 페이지에 저자 명단이 포함되어 있으니 해당 페이지를 참고하시기 바랍니다.) **

📝 Abstract

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 .

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