ICCV 2025 | RobustSplat: 解耦致密化与动态的抗瞬态3DGS三维重建

Core Viewpoint - The article discusses the RobustSplat method, which addresses the challenges of 3D Gaussian Splatting (3DGS) in rendering dynamic objects while maintaining high-quality static scene reconstruction [1][4][19]. Research Motivation - The motivation stems from understanding the dual role of Gaussian densification in 3DGS, which enhances scene detail but can lead to overfitting in dynamic areas, resulting in artifacts and scene distortion [4][6]. Methodology - Transient Mask Estimation: Utilizes a Mask MLP architecture to output pixel-wise transient masks, distinguishing between transient and static regions [9]. - Feature Selection: DINOv2 features are chosen for their balance of semantic consistency, noise resistance, and computational efficiency, outperforming other feature sets [10]. - Supervision Design: Combines image residual loss and feature cosine similarity loss for mask optimization, enhancing dynamic area recognition [10]. - Delayed Gaussian Growth Strategy: This core strategy postpones the densification process to prioritize static scene structure optimization, reducing the risk of misclassifying static areas as transient [12]. - Mask Regularization: Aims to minimize the misclassification of static regions during early optimization stages [12]. - Scale Cascade Mask Guidance: Initially estimates transient masks using low-resolution features, transitioning to high-resolution supervision for improved accuracy [14]. Experimental Results - Experiments on NeRF On-the-go and RobustNeRF datasets show that RobustSplat outperforms baseline methods like 3DGS, SpotLessSplats, and WildGaussians in PSNR, SSIM, and LPIPS metrics [16][20]. Conclusion - The RobustSplat method effectively reduces rendering artifacts caused by transient objects while preserving scene details, demonstrating its robustness in complex scenarios [18][19].