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3D point clouds collected by low-channel light detection and ranging (LiDAR) are relatively sparse compared to high-channel LiDAR, which is considered costly. To address this, an outdoor large-scene point cloud reconstruction (LSPCR) technique based on transformer is proposed in this study. The LSPCR approach first projects the original sparse 3D point cloud onto a 2D range image; then, it enhances the resolution in the vertical direction of the 2D range image before converting the high-resolution range image back to a 3D point cloud as the final reconstructed point cloud data. Experiments were performed on the real-world KITTI dataset, and the results show that LSPCR achieves an average accuracy improvement of over 60% compared to non-deep-learning algorithms; it also achieves better performance compared to the latest deep-learning algorithms. Therefore, LSPCR is an effective solution for sparse point cloud reconstruction and addresses the challenges associated with high-resolution LiDAR point clouds. [ABSTRACT FROM AUTHOR]