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Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting

Shuangkang Fang, I-Chao Shen, Xuanyang Zhang, Zesheng Wang, Yufeng Wang, Wenrui Ding, Gang Yu, Takeo Igarashi

TL;DR

This work proposes DropAnSH-GS, a novel anchor-based Dropout strategy that substantially outperforms existing Dropout methods with negligible computational overhead, and can be readily integrated into various 3DGS variants to enhance their performances.

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

Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting

TL;DR

This work proposes DropAnSH-GS, a novel anchor-based Dropout strategy that substantially outperforms existing Dropout methods with negligible computational overhead, and can be readily integrated into various 3DGS variants to enhance their performances.

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
Paper Structure (18 sections, 6 equations, 8 figures, 10 tables)

This paper contains 18 sections, 6 equations, 8 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Preliminaries
  5. Pilot Study
  6. Anchor-based Dropout
  7. Spherical Harmonics Dropout
  8. Loss Function
  9. Experiments
  10. Implementation Details
  11. Comparison Results
  12. Compression of Model Size
  13. Compatibility
  14. Training Efficiency
  15. Ablation Study
  16. ...and 3 more sections

Figures (8)

  • Figure 1: Difference between DropGaussian park2025dropgaussian and our DropAnSH-GS. (a) DropGaussian randomly sets the opacity of individual Gaussians to zero. (b) DropAnSH-GS selects random Gaussians as anchors, discards their neighbors, and applies Dropout to high-degree SH. Anchor-based Dropout eliminates contiguous 3D regions, which effectively inhibits adjacent Gaussians from compensating for the dropped ones, forcing remaining Gaussians to learn more robust scene representations, thereby strengthening the Dropout regularization effect. Dropping SH further reduces overfitting and yields a more compact model.
  • Figure 2: Compensation effect in neighbor Gaussians. (a) We measure the spatial autocorrelation of opacity and color between Gaussians at varying distances using Moran’s I metric moran1950notesmihajlovic2025splatfields. Closer Gaussians exhibit higher similarity in opacity and color. This spatial redundancy implies that dropping only individual Gaussians can be easily compensated for by their surrounding similar neighbors, thereby limiting the effectiveness of dropout. (b) We report the Mean Absolute Error between rendered images before and after applying two different Dropout strategies, S1 and S2. When dropping a similar number of Gaussians, the strategy that drops Gaussians and their neighboring Gaussians (S1) has a larger impact on rendering quality, as it avoids simple compensation by neighboring Gaussians. (c) In regions surrounding the dropped Gaussians, the S1 strategy activates a greater number of remaining Gaussians, resulting in stronger gradient updates.
  • Figure 3: Overfitting caused by high-degree SH. On the LLFF dataset, (a) under full-view conditions, moderately increasing the degree of spherical harmonics can improve the performance of the 3DGS model. However, (b) under sparse-view settings, using high-degree spherical harmonics leads to performance degradation and a significant increase in model size, indicating that spherical harmonics themselves also constitute a source of overfitting.
  • Figure 4: Overview of DropAnSH-GS. During the training phase, we randomly select a set of anchor Gaussians and drop these anchors and their surrounding neighbours within a local spatial region, thereby reducing the likelihood of neighboring Gaussians compensating for one another and enhancing the regularization effect. Simultaneously, high-degree SH coefficients are randomly dropped to further strengthen regularization and yield a more compact model representation.
  • Figure 5: Qualitative comparison on the LLFF dataset (3 views).
  • ...and 3 more figures