T-FastGS: Transient-Robust and Efficient 3D Gaussian Splatting

Transient objects break the multi-view consistency assumption of 3D Gaussian Splatting (3DGS), leading to degraded reconstruction quality. Existing methods struggle to yield robust reconstruction in the presence of transients while preserving high training efficiency. To tackle this challenge, we present T-FastGS, a transient-robust and efficient reconstruction framework. We find that transient content introduces ambiguity into error-aware Gaussian density control. To mitigate this problem, we propose Mask-Guided Error-Aware Density Control (MGDC), which constructs reliable cues for density adjustment by distinguishing static and transient regions. Moreover, we identify a timescale mismatch between transient mask learning and Gaussian optimization. Motivated by this finding, we introduce an Asynchronous Alternating Optimization (AAO) scheme that cuts redundant updates to the mask predictor and retains training efficiency. Experiments show that T-FastGS achieves competitive rendering quality with state-of-the-art transient-free 3DGS methods on two benchmark datasets, while delivering around 3× or higher training speedup over these methods.