This invention discloses a method for constructing a realistic depth
estimation training dataset based on a 3D mesh and 3D
Gaussian splashing, relating to the field of UAV aerial surveying technology. The method involves acquiring
aerial image sequences using a visible light camera mounted on a UAV platform, and filtering and preprocessing the
image quality; generating a dense
point cloud and a 3D mesh; training and generating a 3DGS model based on camera
pose and image sequences consistent with the 3D mesh; unifying the 3D mesh, camera
pose, and 3DGS model to the same world coordinate
system; and using Z-buffer rasterization to output pixel-by-pixel depth and validity masks; generating RGB images of the corresponding viewpoint in the 3DGS renderer; and encapsulating the dataset according to a recommended data organization structure. This invention can accurately generate realistic RGB and geometrically accurate depth, with strict pixel alignment between the RGB images and depth maps, adapting to the needs of real-world application scenarios such as UAV aerial surveying and actual
terrain mapping.