Currently， LiDAR- and vision-based 3D reconstruction technologies are widely used in terrain scene measurement. Although various 3D imaging methods based on LiDAR and cameras have been developed， each has limitations. RGB-D cameras can capture both color and depth information but often have lower accuracy than LiDAR， while 3D LiDAR provides high-precision spatial data but lacks color information and is typically expensive. This paper proposes a 3D-RGB imaging method based on data-level fusion of a 2D LiDAR and multi-view cameras， integrating 3D-RGB point cloud data from a 2D LiDAR and four cameras from different viewpoints. The method achieves accurate and dense 3D-RGB imaging through 3D-RGB point enhancement， feature plane detection and extraction， and global consistency alignment. First， fusing RGB and point cloud data enhances data quality， while feature plane detection optimizes geometric structure representation. Then， a global consistency alignment strategy reduces accumulated errors and improves overall imaging accuracy. Experimental results show that compared with multi-line LiDAR solutions， the proposed method offers advantages in imaging density and accuracy， with an overall error of less than 0.15 m， demonstrating its potential for 3D reconstruction and environmental surveying in complex environments.