We introduce Geo-NVS-w, a geometry-aware framework for high-fidelity novel view synthesis from unstructured, in-the-wild image collections. While existing in-the-wild methods already excel at novel view synthesis, they often lack geometric grounding on complex surfaces, sometimes producing results that contain inconsistencies. Geo-NVS-w addresses this limitation by leveraging an underlying geometric representation based on a Signed Distance Function (SDF) to guide the rendering process. This is complemented by a novel Geometry-Preservation Loss which ensures that fine structural details are preserved. Our framework achieves competitive rendering performance, while demonstrating a 4-5x reduction reduction in energy consumption compared to similar methods. We demonstrate that Geo-NVS-w is a robust method for in-the-wild NVS, yielding photorealistic results with sharp, geometrically coherent details.

翻译：我们提出了Geo-NVS-w，一个几何感知的框架，用于从非结构化的野外图像集合中进行高保真新视角合成。现有的野外方法虽然在新视角合成方面已表现出色，但往往缺乏对复杂表面的几何基础，有时会产生包含不一致性的结果。Geo-NVS-w通过利用基于符号距离函数（SDF）的底层几何表示来指导渲染过程，从而解决了这一局限。这辅以一种新颖的几何保持损失，确保精细的结构细节得以保留。我们的框架实现了具有竞争力的渲染性能，同时与类似方法相比，能耗降低了4-5倍。我们证明，Geo-NVS-w是一种鲁棒的野外新视角合成方法，能够生成具有清晰、几何一致细节的逼真结果。