Horizon-GS：面向大规模空对地场景的统一3D Gaussian Splatting
Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes
AI总结：
针对航拍与街景图像难以统一重建的问题，本文提出基于 Gaussian Splatting 的 Horizon-GS，通过新训练策略克服视角差异，并构建空对地数据集，实验验证其有效性。
摘要：
在神经场景重建与渲染中，无缝融合航拍图像与街景图像仍是一项重大挑战。现有方法主要聚焦于单一域，限制了其在沉浸式环境中的应用；这类环境要求在水平和垂直方向均发生较大视角变化的情况下进行广泛的自由视角探索。我们提出 Horizon-GS，这是一种基于 Gaussian Splatting 技术构建的新方法，用于处理航拍与街景的统一重建和渲染。该方法通过一种新的训练策略应对融合这些视角的关键挑战，克服视点差异以生成高保真场景。我们还构建了一个包含合成场景与真实场景的高质量空对地视图数据集，以推动进一步研究。在多种城市场景数据集上的实验证实了本方法的有效性。
英文摘要：
Seamless integration of both aerial and street view images remains a significant challenge in neural scene reconstruction and rendering. Existing methods predominantly focus on single domain, limiting their applications in immersive environments, which demand extensive free view exploration with large view changes both horizontally and vertically. We introduce Horizon-GS, a novel approach built upon Gaussian Splatting techniques, tackles the unified reconstruction and rendering for aerial and street views. Our method addresses the key challenges of combining these perspectives with a new training strategy, overcoming viewpoint discrepancies to generate high-fidelity scenes. We also curate a high-quality aerial-to-ground views dataset encompassing both synthetic and real-world scene to advance further research. Experiments across diverse urban scene datasets confirm the effectiveness of our method.
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