We propose Self-Augmented Residual 3D Gaussian Splatting (SA-ResGS), a novel framework to stabilize uncertainty quantification and enhancing uncertainty-aware supervision in next-best-view (NBV) selection for active scene reconstruction. SA-ResGS improves both the reliability of uncertainty estimates and their effectiveness for supervision by generating Self-Augmented point clouds (SA-Points) via triangulation between a training view and a rasterized extrapolated view, enabling efficient scene coverage estimation. While improving scene coverage through physically guided view selection, SA-ResGS also addresses the challenge of under-supervised Gaussians, exacerbated by sparse and wide-baseline views, by introducing the first residual learning strategy tailored for 3D Gaussian Splatting. This targeted supervision enhances gradient flow in high-uncertainty Gaussians by combining uncertainty-driven filtering with dropout- and hard-negative-mining-inspired sampling. Our contributions are threefold: (1) a physically grounded view selection strategy that promotes efficient and uniform scene coverage; (2) an uncertainty-aware residual supervision scheme that amplifies learning signals for weakly contributing Gaussians, improving training stability and uncertainty estimation across scenes with diverse camera distributions; (3) an implicit unbiasing of uncertainty quantification as a consequence of constrained view selection and residual supervision, which together mitigate conflicting effects of wide-baseline exploration and sparse-view ambiguity in NBV planning. Experiments on active view selection demonstrate that SA-ResGS outperforms state-of-the-art baselines in both reconstruction quality and view selection robustness.

翻译：我们提出自增强残差三维高斯溅射（SA-ResGS），这是一种用于稳定不确定性量化并增强主动场景重建中下一最佳视角（NBV）选择的不确定性感知监督的新颖框架。SA-ResGS通过训练视图与栅格化外推视图之间的三角测量生成自增强点云（SA-Points），实现了高效的场景覆盖估计，从而同时提升了不确定性估计的可靠性及其监督的有效性。在通过物理引导的视角选择改善场景覆盖的同时，SA-ResGS还针对稀疏且宽基线视图所加剧的欠监督高斯分布问题，首次引入了专为三维高斯溅射设计的残差学习策略。该定向监督通过将不确定性驱动过滤与受dropout和难负样本挖掘启发的采样相结合，增强了高不确定性高斯分布中的梯度流。我们的贡献有三方面：（1）一种基于物理的视角选择策略，促进高效且均匀的场景覆盖；（2）一种不确定性感知的残差监督方案，可放大对贡献较弱的高斯分布的学习信号，从而提升训练稳定性并改善不同相机分布场景下的不确定性估计；（3）作为受限视角选择与残差监督的共同结果，实现了不确定性量化的隐式去偏，这共同缓解了NBV规划中宽基线探索与稀疏视图模糊性之间的冲突效应。在主动视角选择上的实验表明，SA-ResGS在重建质量和视角选择鲁棒性方面均优于现有先进基线方法。