Vertical slice equal sampling and transformer network for point cloud ground filtering

Ground filtering is a critical yet challenging aspect of Digital Elevation Model (DEM) generation from LiDAR point clouds. Existing deep learning approaches for point cloud processing encounter difficulties in addressing the conflict between the demands of large-scale object filtering tasks and GPU memory. In response to this challenge, this study presents a novel ground filtering method utilizing deep learning. The proposed method incorporates Vertical Slicing Equally Sampling (VSES) to locally sample the original point cloud, effectively organizing the unordered sequence of points and reducing their number while maintaining the representation of the terrain. Subsequently, the resampled point cloud is fed into the TransGF network for classification processing. TransGF is a network model that leverages Transformer as its backbone. Initially, TransGF employs a linear layer to project the input data from a low-dimensional space of (n, 3) to a high-dimensional space of (n, 32). It then extracts features using a module comprising six consecutive Transformer blocks. Finally, TransGF outputs the probability of each point in the samples belonging to either ground or non-ground points. This method is evaluated using the publicly accessible OpenGF ground filtering dataset, showcasing its superior performance in testing scenarios. Noteworthy advantages include structural conciseness, ease of training, and a promising potential for practical applications. The results affirm the effectiveness of TransGF in overcoming challenges associated with ground filtering in LiDAR point cloud processing, positioning it as a valuable contribution to the advancement of DEM generation techniques.