统一重建空地视角场景，实现无缝自由视角渲染。
Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes
- 基于高斯点云扩散技术，融合空中与街景图像
- 在多个城市数据集上实现高质量场景重建
- 适合需要大范围视点变化的沉浸式应用
无缝整合空中与街景图像仍是神经场景重建与渲染中的重大挑战。现有方法多聚焦单一视角，限制了在需大幅水平与垂直视点变化的沉浸式环境中的应用。我们提出 Horizon-GS，一种基于高斯点云扩散技术的新方法，实现空中与街景的统一重建与渲染。该方法通过新训练策略克服视角差异，生成高保真场景。同时，我们构建了一个包含合成与真实场景的高质量空地视角数据集，以推动后续研究。在多个城市场景数据集上的实验验证了方法的有效性。
原文摘要 · Abstract (English)
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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