基於錨點與球諧函數的稀疏視角高斯潑濺

摘要

近期提出的3D高斯潑濺（3DGS）Dropout方法通過隨機歸零高斯不透明度來解決稀疏視角下的過擬合問題。然而，我們發現這類方法存在鄰域補償效應：被丟棄的高斯常被其相鄰高斯補償，從而削弱了正則化效果。此外，這些方法忽略了高階球諧係數（SH）對過擬合的影響。為解決這些問題，我們提出DropAnSH-GS——一種新穎的基於錨點的Dropout策略。與獨立丟棄高斯的方式不同，我們的方法隨機選取特定高斯作為錨點，並同步移除其空間鄰域高斯。這種機制有效破壞了錨點附近的局部冗餘性，促使模型學習更具魯棒性的全局表徵。進一步地，我們將Dropout擴展至顏色屬性，通過隨機丟棄高階SH係數將外觀信息集中於低階SH。此策略不僅強化了過擬合抑制效果，還能通過SH截斷實現訓練後模型的靈活壓縮。實驗結果表明，DropAnSH-GS以可忽略的計算開銷顯著優於現有Dropout方法，且能無縫集成到各類3DGS變體中提升其性能。項目網站：https://sk-fun.fun/DropAnSH-GS

English

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

基於錨點與球諧函數的稀疏視角高斯潑濺

Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting

摘要

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