ARSGaussian: 3D Gaussian Splatting with LiDAR for Aerial Remote Sensing Novel View Synthesis
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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....