Shape-Selective Splatting: Regularizing the Shape of Gaussian for Sparse-View Rendering

Gun Ryu, Wonjun Kim

Abstract: In recent years, 3D Gaussian splatting (3DGS) has shown high-fidelity rendering results in real-time. However, 3DGS often encounters the overfitting problem under sparse-view conditions due to insufficient cross-view constraints. In this letter, to mitigate this limitation, we focus on the effect of Gaussian shapes on the scene reconstruction from sparse input views. The key idea is to allow each Gaussian to adaptively select its shape in accordance with the scene structure. Specifically, we propose to put a learnable parameter into Gaussian attributes, which indicates the probability of each shape. This indicator is optimized with other attributes while making each Gaussian change its shape to 1D, 2D, and 3D for representing edges, planar surfaces, and volumetric regions, respectively. Based on a geometrically accurate representation, the proposed method consequently alleviates the model from overfitting to a limited set of training views. Furthermore, we apply a depth regularization scheme within a set of selected pixels to precisely constrain positions of Gaussians. Experimental results on benchmark datasets show that the proposed method effectively improves the performance of novel view synthesis under sparse input views.

External IDs:dblp:journals/spl/RyuK25