Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large computational cost of volumetric rendering. More recently, 3D Gaussian Splatting emerged as a much faster alternative with superior rendering quality and training efficiency, especially for small-scale and object-centric scenarios. Nevertheless, this technique suffers from poor performance on unstructured in-the-wild data. To tackle this, we extend over 3D Gaussian Splatting to handle unstructured image collections. We achieve this by modeling appearance to seize photometric variations in the rendered images. Additionally, we introduce a new mechanism to train transient Gaussians to handle the presence of scene occluders in an unsupervised manner. Experiments on diverse photo collection scenes and multi-pass acquisition of outdoor landmarks show the effectiveness of our method over prior works achieving state-of-the-art results with improved efficiency.

翻译：隐式神经表示方法在从非结构化野照片集合中学习三维场景方面取得了显著进展，但受限于体积渲染的巨大计算成本。最近，三维高斯泼溅作为一种更快速的替代方案出现，具有卓越的渲染质量和训练效率，尤其适用于小规模、对象中心的场景。然而，该技术在非结构化野数据上表现不佳。为解决此问题，我们对三维高斯泼溅进行了扩展以处理非结构化图像集合。通过建模外观来捕捉渲染图像中的光度变化实现这一目标。此外，我们引入了一种新机制来以无监督方式训练瞬态高斯体，以处理场景遮挡物的存在。在不同照片集合场景及多通道采集的户外地标上的实验表明，我们的方法相较于先前工作具有有效性，在提升效率的同时达到了最先进的结果。