Recent advances in 3D Gaussian Splatting (3DGS) have achieved state-of-the-art results for novel view synthesis. However, efficiently capturing high-fidelity reconstructions of specific objects within complex scenes remains a significant challenge. A key limitation of existing active reconstruction methods is their reliance on scene-level uncertainty metrics, which are often biased by irrelevant background clutter and lead to inefficient view selection for object-centric tasks. We present OUGS, a novel framework that addresses this challenge with a more principled, physically-grounded uncertainty formulation for 3DGS. Our core innovation is to derive uncertainty directly from the explicit physical parameters of the 3D Gaussian primitives (e.g., position, scale, rotation). By propagating the covariance of these parameters through the rendering Jacobian, we establish a highly interpretable uncertainty model. This foundation allows us to then seamlessly integrate semantic segmentation masks to produce a targeted, object-aware uncertainty score that effectively disentangles the object from its environment. This allows for a more effective active view selection strategy that prioritizes views critical to improving object fidelity. Experimental evaluations on public datasets demonstrate that our approach significantly improves the efficiency of the 3DGS reconstruction process and achieves higher quality for targeted objects compared to existing state-of-the-art methods, while also serving as a robust uncertainty estimator for the global scene.

翻译：三维高斯泼溅（3DGS）技术的最新进展已在新颖视角合成任务中取得了最先进的效果。然而，在复杂场景中高效捕获特定对象的高保真重建仍面临重大挑战。现有主动重建方法的一个关键局限在于其依赖场景级不确定性度量，这类度量常受无关背景干扰而产生偏差，导致面向对象任务的视角选择效率低下。本文提出OUGS框架，通过为3DGS构建更具原则性、物理基础的不确定性表述来解决这一难题。我们的核心创新在于直接从三维高斯基元（如位置、尺度、旋转）的显式物理参数推导不确定性，通过渲染雅可比矩阵传播这些参数的协方差，建立具有高度可解释性的不确定性模型。在此基础上，我们无缝集成语义分割掩码以生成针对性的对象感知不确定性评分，从而有效解耦目标对象与其所处环境。这使得主动视角选择策略能优先选择对提升对象保真度至关重要的视角。在公开数据集上的实验评估表明，相较于现有最先进方法，我们的方法显著提升了3DGS重建过程的效率，并针对目标对象获得了更高质量的重建结果，同时还能作为全局场景的鲁棒不确定性估计器。