TD-GS: Few-shot Object View Synthesis via Task-Disentangled 3D Gaussian Splatting

Abstract

3D Gaussian Splatting (3D-GS) has exhibited impressive progress in novel view synthesis. When given the sparse views, its performance degrades severely, causing many problems like novel views collapse and excessive floaters. Many recent methods take into account fitting input views, inferring missing scene information and optimizing the final scene representation, all through a single stage. After revisiting the task, we propose a novel framework, Task-Disentangled 3D Gaussian Splatting, abbreviated to TD-GS. It splits the sparse views synthesis task into two subtasks: (i) Dense Generation. (ii) Enhanced Synthesis. In the subtask of Dense Generation, we estimate dense views from sparse input. Then in the subtask of Enhanced Synthesis, both the dense views and the sparse input participate in the training of Gaussians to obtain the final Gaussian representation of the scene. In the process of completing the first subtask, we carefully design Gaussian Cloud Denoising to directly edit 3D Gaussians. Also, we introduce two regularization methods to guide the geometric optimization towards an optimal solution. The purpose is to estimate more reliable outputs. Many experiments have validated that our TD-GS outperforms other state-of-the-art methods.