Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting

TL;DR

This paper introduces DropAnSH-GS, a novel dropout method for 3D Gaussian Splatting that uses anchor-based neighbor removal and spherical harmonic truncation to reduce overfitting and improve model robustness.

Contribution

The paper proposes DropAnSH-GS, a new anchor-based dropout strategy that disrupts local redundancies and extends dropout to spherical harmonic coefficients, enhancing 3D Gaussian Splatting performance.

Findings

Outperforms existing dropout methods with negligible overhead

Effectively reduces overfitting in sparse-view 3D Gaussian Splatting

Enables flexible post-training model compression via SH truncation

Abstract

Recent 3D Gaussian Splatting (3DGS) Dropout methods address overfitting under sparse-view conditions by randomly nullifying Gaussian opacities. However, we identify a neighbor compensation effect in these approaches: dropped Gaussians are often compensated by their neighbors, weakening the intended regularization. Moreover, these methods overlook the contribution of high-degree spherical harmonic coefficients (SH) to overfitting. To address these issues, we propose DropAnSH-GS, a novel anchor-based Dropout strategy. Rather than dropping Gaussians independently, our method randomly selects certain Gaussians as anchors and simultaneously removes their spatial neighbors. This effectively disrupts local redundancies near anchors and encourages the model to learn more robust, globally informed representations. Furthermore, we extend the Dropout to color attributes by randomly dropping higher-degree SH to concentrate appearance information in lower-degree SH. This strategy further mitigates overfitting and enables flexible post-training model compression via SH truncation. Experimental results demonstrate that DropAnSH-GS substantially outperforms existing Dropout methods with negligible computational overhead, and can be readily integrated into various 3DGS variants to enhance their performances. Project Website: https://sk-fun.fun/DropAnSH-GS