When processing point cloud slicing data with multiple contour boundaries, the existing boundary point sorting algorithms have problems such as the inability to distinguish each boundary, generation of abnormal boundary polygons, and cross-sectional area calculation errors, which result in low volume measurement accuracy. Therefore, an improved point cloud slicing method that considers multiple contour boundary segmentation is proposed in this paper for high-accuracy volume measurement of irregular objects. In this method, the multiple boundaries are segmented by the segmentation based on euclidean clustering (SEC) method or the polygon splitting and recombination (PSR) method. Then, the inclusion relationships of the boundary polygons are clarified by the PNPoly algorithm, and the cross-sectional areas are calculated. Finally, the volume of the point cloud (that is, object volume) is calculated by accumulating the cross-sectional areas according to the slicing order. Multiple datasets are used to compare and analyze the effectiveness and correctness of the proposed two boundary segmentation methods as well as their volume measurement accuracy and efficiency. Experimental results demonstrate that the PSR method has high boundary segmentation accuracy, strong applicability, stable and reliable volume measurement accuracy (relative errors of volume calculation on three datasets are 0.0901%, 0.0557%, and 0.0289%, respectively), and less calculation time (with calculation time of 2.229 s, 33.732 s, and 327.476 s, respectively), thereby achieving the purpose of high-precision volume measurement.