LeOp-GS: Learned Optimizer with Dynamic Gradient Update for Sparse-View 3DGS.

Abstract

3D Gaussian Splatting (3DGS) achieves remarkable speed and performance in novel view synthesis but suffers from overfitting and degraded reconstruction when handling sparse-view inputs. This paper innovatively addresses this challenge from a learning-to-optimize perspective by leveraging a learned optimizer (i.e., a multi-layer perceptron, MLP) to update the relevant parameters of 3DGS during the optimization process. Evidently, using a single MLP to handle all optimization variables, whose numbers may even vary during the optimization process, is impossible. Therefore, we present a point-wise position-aware optimizer that updates the parameters for each 3DGS point individually. Specifically, it takes the point coordinates and corresponding parameter values as input to predict the updates, thereby allowing efficient and adaptive optimization. In the case of sparse view modeling, the learned optimizer imposes position-aware constraints on the parameter updates during optimization. This effectively encourages the relevant parameters to converge stably to better solutions. To update the optimizer's parameters, we propose a dynamic gradient update strategy based on spatial perturbation and weighted fusion, enabling the optimizer to capture broader contextual information. Experiments demonstrate that our method effectively addresses the problem of modeling 3DGS from sparse training views, achieving state-of-the-art results across multiple datasets.