TwinDropGS: Rethinking Dropout Regularization for Sparse-View Gaussian Splatting with Twin Consistency

Sparse-view novel view synthesis is a fundamental yet challenging problem in 3D reconstruction, where limited observations often lead to severe overfitting and unreliable scene representations. Recent dropout-based 3D Gaussian Splatting methods alleviate this issue by randomly removing Gaussian primitives during training. However, these methods typically optimize only a single stochastic Gaussian subfield at each iteration, leaving the discrepancy across different dropout realizations unobserved and unconstrained. In this work, we rethink dropout regularization from a consistency-centric perspective and propose TwinDropGS, a Twin Dropout-induced Consistency Regularization framework for sparse-view Gaussian Splatting. To make dropout-induced stochastic discrepancy explicitly observable and regularizable, we first construct twin Gaussian subfields from a shared Gaussian field while ensuring complete joint Gaussian coverage. Building on it, we introduce an error-guided low-frequency consistency regularization that emphasizes regions with larger reconstruction errors and aligns their low-frequency luminance responses, avoiding excessive constraints on ambiguous high-frequency details. Additionally, a progressive complementary consistency scheduling strategy gradually increases both subfield complementarity and consistency strength throughout training, enabling stable structure formation in early optimization and stronger stochastic regularization in later stages. Extensive experiments demonstrate that TwinDropGS consistently improves reconstruction quality and training stability over existing dropout-based 3DGS methods while retaining a simple and plug-and-play design. The source code will be released at http://github.com.