Novel View Synthesis (NVS) can reconstruct scenes from multi-view images and synthesize novel images from new viewpoints, which provides technical support for tasks such as target recognition and environmental perception. Aerial remote sensing can conveniently capture a wealth of multi-view images with just a few flights. However, the challenges brought by large distances and sparse viewing angles during collection can cause the model to easily produce floaters and overgrowth issues due to geometric estimation errors. This results in low visual quality and a lack of precise geometric estimation capabilities. Therefore, this study presents ARSGaussian, an innovative novel view synthesis (NVS) method for aerial remote sensing. The method incorporates LiDAR point cloud as constraints into the 3D Gaussian Splatting approach, adaptively guiding the Gaussians to grow and split along geometric benchmarks, thereby addressing the overgrowth and floaters issues. Additionally, considering the geometric distortions arising from data acquisition, coordinate transformations with distortion parameters are integrated to replace the simple pinhole camera model parameters to achieve pixel-level alignment between LiDAR point cloud and multi-view optical images, facilitating the accurate fusion of heterogeneous data and achieving the high-precision geo-alignment. Moreover, depth, normal and scale consistency losses are introduced into the regularization process to guide Gaussians toward real depth and plane representations, significantly improving geometric estimation accuracy. To address the current lack of dense airborne hybrid datasets, we have established and released AIR-LONGYAN, an open-source dataset containing a dense LiDAR point cloud (8 pts/m) and multi-view optical images captured by airborne scanners and cameras in diverse scenes....

翻译：新视角合成（NVS）能够从多视角图像重建场景并合成新视点的图像，为目标识别、环境感知等任务提供技术支持。航空遥感仅需少量飞行即可便捷地获取丰富的多视角图像。然而，采集过程中大距离和稀疏视角带来的挑战，容易导致模型因几何估计误差而产生漂浮物和过度生长问题，造成视觉质量低下且缺乏精确的几何估计能力。为此，本研究提出ARSGaussian，一种面向航空遥感的创新性新视角合成方法。该方法将LiDAR点云作为约束引入三维高斯溅射框架，自适应地引导高斯函数沿几何基准生长与分裂，从而解决过度生长和漂浮物问题。此外，考虑到数据采集过程中产生的几何畸变，我们整合了带畸变参数的坐标变换以替代简单的针孔相机模型参数，实现LiDAR点云与多视角光学图像之间的像素级对齐，促进异质数据的精确融合并达成高精度地理配准。同时，在正则化过程中引入深度、法向量和尺度一致性损失，引导高斯函数逼近真实深度与平面表示，显著提升几何估计精度。针对当前缺乏密集机载混合数据集的问题，我们建立并开源了AIR-LONGYAN数据集，该数据集包含密集LiDAR点云（8点/平方米）以及由机载扫描仪和相机在多样化场景中采集的多视角光学图像。