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.
@article{arxiv.2601.08371,
title = {Geo-NVS-w: Geometry-Aware Novel View Synthesis In-the-Wild with an SDF Renderer},
author = {Anastasios Tsalakopoulos and Angelos Kanlis and Evangelos Chatzis and Antonis Karakottas and Dimitrios Zarpalas},
journal= {arXiv preprint arXiv:2601.08371},
year = {2026}
}
Comments
Presented at the ICCV 2025 Workshop on Large Scale Cross Device Localization