<p>The truck loading plays an important role in the open-pit mining industry, underload can reduce work efficiency, overload can affect truck life and even cause some safety issues. Therefore, how to accurately estimate the loading state (loading volume) of the dump truck has become an urgent problem to be solved. An intelligent truck loading volume estimation scheme is proposed to estimate the loading volume, which includes the point cloud extraction of ore pile in the truck and the its surface reconstruction. Firstly, the Euclidean clustering extraction method and color-based region growth segmentation algorithm are used to obtain a single and complete ore pile point cloud. Secondly, the Kriging interpolation method is adopted to reconstruct the ore pile surface, and then the loading volume is estimated based on the reconstructed surface. Finally, the experiments are conducted on the scale model of the electric shovel and dump truck, and experimental results show that the proposed scheme can accurately estimate the volume of truck loading, which contributes to improve efficiency and safety of the truck loading.</p>