CleanSplat: curriculum structural Gaussian splatting for spot-free novel view synthesis

3D Gaussian splatting (3DGS) has gained significant attention for its real-time, photorealistic rendering in novel view synthesis. However, its performance degrades severely when applied to real-world scenes with transients that break cross-view consistency. Existing methods typically attempt to identify and mask out these transients, but inherent masking errors often leave behind incoherent floating Gaussians, resulting in spotted artifacts that degrade the final scene quality. To address these limitations, we propose CleanSplat, a curriculum structural Gaussian splatting method that purifies the scene Gaussians through curriculum 3DGS optimization for transient removal and structural pruning of spotted artifacts. We introduce a curriculum-guided masking paradigm that generates coarse-to-fine transient masks from multi-scale features. The progressive optimization is driven by modulating the masking supervision based on current training state. To clear the spots, we propose a structure-aware handling strategy that employs a superpoint graph (SPG) partitioning of the Gaussians to perform principled identification and hierarchical pruning. This allows for the filtering of both intra-superpoint outliers and entire spurious superpoints based on local 3D coherence instead of only simple photometric consistency. By integrating curriculum 3DGS optimization and structural pruning, our method effectively separates the transients and purifies the static scene Gaussians. Extensive experiments on challenging datasets demonstrate that CleanSplat significantly outperforms state-of-the-art methods, delivering more detailed and cleaner novel view synthesis.