Purpose of Review The rapid development of remote sensing technology hasmade dense 3D data available from airborne laser scanning and recently also photogrammetric point clouds. This paper reviews methods for extraction of individual trees from 3D data and their applications in forestry and ecology. Recent Findings Methods for analysis of 3D data at tree level have been developed since the turn of the century. The first algorithms were based on 2D surface models of the upper contours of tree crowns. These methods are robust and provide information about the trees in the top-most canopy. There are also methods that use the complete 3D data. However, development of these 3D methods is still needed to include use of geometric properties. To detect a large fraction of the tallest trees, a surface model method generally gives the best results, but detection of smaller trees below the top-most canopy requires methods utilizing the whole point cloud. Several new sensors are now available with capability to describe the upper part of the canopy, which can be used to frequently update vegetation maps. Highly sensitive laser photo detectors have become available for civilian applications, which will enable acquisition of high-resolution 3D laser data for large areas to much lower costs. Summary Methods for ITC delineation from 3D data provide information about a large fraction of the trees, but there is still a challenge to make optimal use of the information from whole point cloud. Newly developed sensors might make ITC methods cheaper and feasible for large areas.