用SDF几何结构提升野外图像新视角合成的精度与效率
Geo-NVS-w: Geometry-Aware Novel View Synthesis In-the-Wild with an SDF Renderer
- 基于SDF构建几何表示，引导新视角渲染
- 相比同类方法能耗降低4-5倍，细节更清晰
- 适合需要高几何一致性的真实场景重建
我们提出Geo-NVS-w，一种面向无结构野外图像集合的几何感知高保真新视角合成框架。现有野外方法虽能生成良好视图，但在复杂曲面上常缺乏几何约束，导致结果不一致。Geo-NVS-w通过基于符号距离函数（SDF）的底层几何表示，指导渲染过程，并引入新型几何保持损失，确保精细结构得以保留。该框架在渲染性能上表现优异，同时相比类似方法能耗降低4-5倍。实验表明，Geo-NVS-w能稳健生成具有锐利、几何一致细节的逼真结果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。