Comparative Evaluation of Deep-Learning Models for Point Cloud Upsampling: Insights from Indoor Parking Lot Data Set

Upsampling of point clouds is a critical process in three-dimensional data processing, aimed at enhancing the resolution, uniformity, and overall quality of sparse and irregular point cloud data. This task becomes especially necessary in underground parking lots, where factors such as occlusions, reflective surfaces, and limited sensor coverage often result in incomplete or uneven point distributions. Accurate upsampling in these environments can significantly improve downstream tasks such as semantic segmentation, object detection, and spatial mapping by providing denser and more geometrically consistent reconstructions. In this paper, a comprehensive evaluation of five state-of-the-art deep-learning algorithms for point cloud upsampling is presented. Leveraging a novel, high-quality data set captured in a real-world indoor parking lot environment, the performance of these models is systematically assessed under conditions characterized by structural complexity and varying point densities. This comparative study evaluates each model’s ability to address sparsity and non-uniformity while preserving geometric fidelity and achieving uniform distribution in challenging indoor scenarios.