LiDAR is a remote sensing method which produces precise point clouds consisting of millions of geo-spatially located 3D data points. Because of the nature of LiDAR point clouds, it can often be difficult for analysts to accurately and efficiently recognize and categorize objects. The goal of this paper is automatic large-volume object region segmentation in LiDAR point clouds. This efficient segmentation technique is intended to be a pre- processing step for the eventual classification of objects within the point cloud. The data is initially segmented into local histogram bins. This local histogram bin representation allows for the efficient consolidation of the point cloud data into voxels without the loss of location information. Additionally, by binning the points, important feature information can be extracted, such as the distribution of points, the density of points and a local ground. From these local histograms, a 3D automatic seeded region growing technique is applied. This technique performs seed selection based on two criteria, similarity and Euclidean distance to nearest neighbors. The neighbors of selected seeds are then examined and assigned labels based on location and Euclidean distance to a region mean. After the initial segmentation step, region integration is performed to rejoin over-segmented regions. The large amount of points in LiDAR data can make other segmentation techniques extremely time consuming. In addition to producing accurate object segmentation results, the proposed local histogram binning process allows for efficient segmentation, covering a point cloud of over 9,000 points in 10 seconds.