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.
隐式神经表示方法在从非结构化野外照片集学习3D场景方面取得了令人印象深刻的进步，但仍受到体积渲染大量计算成本的限制。最近，3D高斯溅射作为一个更快的替代方法出现，具有更优越的渲染质量和训练效率，特别是对于小规模和以物体为中心的场景。然而，这项技术在处理非结构化野外数据时性能不佳。为了解决这个问题，我们扩展了3D高斯溅射以处理非结构化图像集合。我们通过建模外观来捕获渲染图像中的光度变化来实现这一点。此外，我们引入了一种新的机制，以无监督的方式训练临时高斯，以处理场景遮挡物的存在。在多样化的照片集场景和室外地标的多次采集上的实验显示，我们的方法相比之前的作品更有效，实现了最先进的结果，同时提高了效率。