Randomised compression ratios for effective large point cloud processing using compressive sensing

Effectively navigating the intricacies of extensive 3D point cloud data in urban environments poses a series of formidable computational challenges. These challenges are primarily attributed to the substantial data volume and density inherent in urban settings, the presence of noise and inconsistencies within the collected data, and the constraints imposed by limited transmission bandwidth, which consequently impact storage requirements. This paper introduces an innovative methodology for handling large point cloud datasets, based on concepts from Sparse Signal Processing (SSP), also known as compressive sensing. The proposed approach integrates well known geometric data manipulation such as the Octree to work hand in hand with SSP, as unified method. Through experimental validation using the Santiago Urban Dataset (SUD), we demonstrate the effectiveness of our method in achieving high data fidelity, as measured by Peak Signal-to-Noise Ratio (PSNR) values reaching approximately 60 dB even at substantial compression ratios. Comparative analysis against traditional methods, including those implemented in the widely used Point Cloud Library (PCL), reveals the superior performance of our proposed methodology. The results underscore the robustness and efficiency of our approach, positioning it as a compelling alternative for compressing extensive 3D point cloud data. This has crucial implications for diverse applications, ranging from city planning to rapid and effective disaster response.