：The integration of Airborne LiDAR data and aerial imagery is useful in data interpretation, land monitoring, and 3D reconstruction. As the first step of these tasks, the geometric registration of the two types of data should be conducted to ensure their alignment. The alignment is sometimes difficult because of differences in their data acquisition mechanisms. The LiDAR data is more reliable and more accurate on smooth surfaces like grounds, walls, and roofs which are difficult to extract from aerial imagery. LiDAR points are mostly sparser than the pixels on aerial images. Considering that the a priori ranging error (1~5 cm) of airborne LiDAR data is usually much smaller than the average point distance (10~50 cm), this paper introduced a plane-constrained block adjustment model to align the two types of data, where the planes were obtained by the intersection of corresponding junction structures. The planar constraints were implemented by forcing surrounding LiDAR points to be on the planes. The proposed block adjustment model is a mixture of the conventional POS-aided and self-calibrated bundle adjustment model and two more types of observing equations. One is the distance between image junction structure observations, and reprojection of the spatial junction structure should be zeros. The other is the normal distance between LiDAR points, and the spatial planes obtained by junction structure should be zeros. In this paper, firstly junction structures in object space were solved based on least squares theory. Then, conjugate planes of junction structures in LiDAR points were detected automatically. Finally, the aerial images block adjustment under constraints of junction structure was performed to obtain the precise interior and exterior orientation parameters. The experimental results showed that both the horizontal and the vertical accuracy of the proposed method could reach 1~2 pixels of the aerial images, which was obviously better than the building-corner-based method. In order to probe into the influence of point cloud density, the LiDAR points were thinned randomly before the geometric registration. The results showed that the accuracy of the proposed method was not influenced but the accuracy of building-corner-based method decreased when the point cloud density decreased, especially the horizontal accuracy. In conclusion, the proposed method takes the advantage of the high-ranging accuracy of LiDAR data to reach high registration accuracy and avoids the influence of the point cloud density. When the density of the LiDAR point cloud is low, a high registration accuracy can be reached using the proposed method.