While size distribution has traditionally been the dominant metric in rock fragmentation, studies have shown that both size and shape characteristics are influential in determining energy consumption, equipment wear, flowability, and efficiency. The main objective of this research is to provide a scalable, quantitative, and light-independent method for characterizing the 3D shape of rock fragments. This work leverages an existing deep learning approach that combines LiDAR-based point cloud acquisition with deep learning instance segmentation. Trained on a synthetic 3D labeled dataset, the deep learning model, SoftRock, accurately segments individual rock pieces and provides key shape metrics such as sphericity, angularity, aspect ratio, and longest dimension for each rock in the pile. The model's performance was then validated on three distinct rock piles curated to represent different spectrum of rock types and sizes, including blasted limestone from a quarry, rounded pebbles, and crushed copper ore from a conveyor belt. The model demonstrated a high level of accuracy across these diverse samples, with the error for key shape metrics ranging from 2% to 16%. While some inaccuracies were observed, primarily due to the sensitivity of sphericity and angularity to noise in the point cloud data, our findings validate the model's ability to capture key shape characteristics. This study provides a foundational framework for integrating comprehensive 3D particle morphology into mining workflows, offering more accurate data to inform decisions that enhance operational efficiency and equipment longevity.