Gaussian splatting enables fast novel view synthesis in static 3D environments. However, reconstructing real-world environments remains challenging as distractors or occluders break the multi-view consistency assumption required for accurate 3D reconstruction. Most existing methods rely on external semantic information from pre-trained models, introducing additional computational overhead as pre-processing steps or during optimization. In this work, we propose a novel method, DeSplat, that directly separates distractors and static scene elements purely based on volume rendering of Gaussian primitives. We initialize Gaussians within each camera view for reconstructing the view-specific distractors to separately model the static 3D scene and distractors in the alpha compositing stages. DeSplat yields an explicit scene separation of static elements and distractors, achieving comparable results to prior distractor-free approaches without sacrificing rendering speed. We demonstrate DeSplat's effectiveness on three benchmark data sets for distractor-free novel view synthesis. See the project website at https://aaltoml.github.io/desplat/.

翻译：高斯泼溅技术能够在静态三维环境中实现快速的新视角合成。然而，真实环境的重建仍面临挑战，因为干扰物或遮挡物会破坏精确三维重建所需的多视角一致性假设。现有方法大多依赖预训练模型提供的外部语义信息，这会在预处理阶段或优化过程中引入额外的计算开销。本研究提出一种新方法DeSplat，该方法仅基于高斯基元的体渲染直接分离干扰物与静态场景元素。我们在每个相机视角内初始化高斯分布，用于重建视角特定的干扰物，从而在alpha合成阶段分别建模静态三维场景与干扰物。DeSplat实现了静态元素与干扰物的显式场景分离，在不牺牲渲染速度的前提下，取得了与现有无干扰物方法相当的结果。我们在三个无干扰物新视角合成基准数据集上验证了DeSplat的有效性。项目网站详见https://aaltoml.github.io/desplat/。