GlORIE-SLAM is a globally optimized RGB-only implicit encoding point cloud SLAM system that achieves high accuracy in mapping and tracking. It uses a deformable neural point cloud representation for scene mapping and integrates online loop closure and global bundle adjustment to maintain global map consistency. The system also incorporates a monocular depth prior to improve reconstruction accuracy and completeness. The DSPO (Disparity, Scale and Pose Optimization) layer optimizes the pose and depth of keyframes along with the scale of the monocular depth, leading to better rendering and mapping performance. GlORIE-SLAM outperforms existing RGB-only SLAM methods in terms of rendering accuracy and tracking performance on the Replica, TUM-RGBD, and ScanNet datasets. The system is efficient and can handle complex and large-scale indoor scenes. The results show that GlORIE-SLAM achieves lower trajectory error and higher rendering accuracy compared to competitive approaches like GO-SLAM. The system benefits from loop closure and online global bundle adjustment, and performs either better or competitive to existing dense neural RGB SLAM methods in tracking, mapping, and rendering accuracy. The source code is available at https://github.com/zhangganlin/Gl0IRE-SLAM.GlORIE-SLAM is a globally optimized RGB-only implicit encoding point cloud SLAM system that achieves high accuracy in mapping and tracking. It uses a deformable neural point cloud representation for scene mapping and integrates online loop closure and global bundle adjustment to maintain global map consistency. The system also incorporates a monocular depth prior to improve reconstruction accuracy and completeness. The DSPO (Disparity, Scale and Pose Optimization) layer optimizes the pose and depth of keyframes along with the scale of the monocular depth, leading to better rendering and mapping performance. GlORIE-SLAM outperforms existing RGB-only SLAM methods in terms of rendering accuracy and tracking performance on the Replica, TUM-RGBD, and ScanNet datasets. The system is efficient and can handle complex and large-scale indoor scenes. The results show that GlORIE-SLAM achieves lower trajectory error and higher rendering accuracy compared to competitive approaches like GO-SLAM. The system benefits from loop closure and online global bundle adjustment, and performs either better or competitive to existing dense neural RGB SLAM methods in tracking, mapping, and rendering accuracy. The source code is available at https://github.com/zhangganlin/Gl0IRE-SLAM.