Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting
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
69a0f96ca13deaa4494488b8
Shuangkang Fang
69a0f96ca13deaa4494488b9
I-Chao Shen
69a0f96ca13deaa4494488ba
Xuanyang Zhang
69a0f96ca13deaa4494488bb
Zesheng Wang
69a0f96ca13deaa4494488bc
Yufeng Wang
69a0f96ca13deaa4494488bd
Wenrui Ding
69a0f96ca13deaa4494488be
Gang Yu
69a0f96ca13deaa4494488bf
Takeo Igarashi
Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting
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
DropAnSH-GS addresses overfitting in 3D Gaussian Splatting by introducing an anchor-based dropout strategy that disrupts local redundancies and extends dropout to color attributes using spherical harmonic coefficients.
3D Gaussian Splatting Dropout Gaussian opacities spherical harmonic coefficients SH anchor-based Dropout spatial neighbors overfitting regularization model compression post-training compression