To address the problems of oversegmentation of micro-small plane structures and undersegmentation of hole edges and smooth transition regions during the point cloud segmentation for vehicle body components in existing algorithms, a density-proportional growth consistency (DPGC) segmentation algorithm is proposed in this study. This algorithm is used to accurately segment complex surface-component point clouds by adhering to the principle of DPGC. The specific methodology involves the following steps: first, performing principal component analysis on the scanning point cloud data to calculate the standard-density point cloud, thereby establishing the algorithm's density-proportional benchmark; second, devising an adaptive point-cloud search radius function model to determine the optimal nearest neighbor search radius for each region, enhancing the segmentation accuracy across different feature regions; third, employing an adaptive radius density segmentation algorithm to preliminary screen plane regions using a density-ratio threshold between the point cloud and principal-projection point cloud; and finally, implementing an equal-scale adaptive radius density segmentation algorithm to compute the local-projection point cloud within the search radius. The segmentation is based on the density ratio between the local-projection point cloud and original cloud as well as the density ratio of the equal-scale region, further refining the nonplane regions to achieve the final segmentation result. Results of comparative tests demonstrate that the DPGC segmentation algorithm has a higher intersection over union and surpasses mainstream algorithms such as RANSAC-LS and improved region growth segmentation algorithm, particularly in areas with strong features such as door frames, thereby effectively achieving accurate point cloud segmentation of body components.