GASplat: Geometry-aware Distractor-free 3D Gaussians Splatting Under Sparse-View Settings

3D Gaussian Splatting (3DGS) achieves real-time photorealistic 3D static scene reconstruction, yet its performance degrades significantly when sparse input images also contain dynamic transient objects. Existing distractor-free reconstruction methods misclassify static hard-to-reconstruct regions as distractors, sparse-view reconstruction methods don't block gradients of distractors and their vanilla combination enhances both static and transient regions. To tackle this new problem, we propose GASplat, a novel framework reconstructs static scene with sparse input containing distractors. Specifically, with aim of better distinguishing static and transient regions to identify distractors with memorization effect, we generate coarse masks through pretrained image matching model and geometry-aware filtering. Then, we enhance initial point clouds of high confident static regions with coarse masks. Next, we recover point clouds and masks of far background regions mitigating limitations of pretrained model. Finally, in order to precisely block gradients from transient objects and refine distractors masks, we design a progressive training schedule to reconstruct static scenes. Extensive experiments on distractors datasets under sparse view settings demonstrate that our approach can detect transient objects given sparse input images, outperforming current state-of-the-art methods.