Abstract: Efficient leaf azimuth angles and plant spacing are crucial for enhancing light interception efficiency in maize, thereby increasing yield per unit area. Traditional methods for measuring these traits are labor-intensive and prone to error. This study aimed to develop an accurate and efficient method for determining leaf azimuth angles and plant spacing in maize to improve understanding of field competition and support breeding programs. Utilizing light detection and ranging (Lidar) technology, 3D point cloud data of maize plants were collected, enabling effective 3D morphological reconstruction through multi-frame stitching. Principal component analysis (PCA) was employed to determine the leaf azimuth angles of individual maize plants. Additionally, a method based on point density analysis was developed to identify the central axis position of single maize plants. Specifically, point density in the neighborhood of each point in the maize point cloud was calculated, with the central axis determined along the direction of highest point density. The integration of PCA-based leaf azimuth detection and point density analysis provided a robust framework for accurately determining leaf azimuth angles and plant spacing. In the detection of leaf azimuth angles, this method achieved an R² of 0.87 and an RMSE of 5.19°. For plant spacing detection, the R² was 0.83 and the RMSE was 0.08 m. This approach facilitates parameterized modeling of field competition, significantly enhancing the efficiency of breeding programs by providing detailed and precise phenotypic data. Despite the high accuracy demonstrated by the proposed methods, further investigation is needed to evaluate their effectiveness under varying environmental conditions and across different maize varieties. Additionally, challenges related to partial occlusions and complex canopy structures may impact the accuracy of point cloud data analysis, necessitating further refinement of the algorithms.