HO-Gaussian is a hybrid optimization method for 3D Gaussian Splatting (3DGS) designed to enhance rendering in urban scenes. It addresses the limitations of traditional 3DGS methods that rely on Structure-from-Motion (SfM) points and struggle with rendering distant, low-texture, and sky areas. HO-Gaussian integrates a grid-based volume with the 3DGS pipeline, eliminating the need for SfM initialization and improving rendering quality through point densification and Gaussian positional and directional encoding. The method also introduces neural warping to ensure consistent rendering across multiple cameras, reducing overfitting to specific viewpoints. Key contributions include a novel pipeline for learning Gaussian positions from a grid-based volume, Gaussian positional and directional encoding to reduce disk space usage, and a neural warping scheme for multi-camera rendering. Experimental results on autonomous driving datasets show that HO-Gaussian achieves photo-realistic rendering in real-time, outperforming both NeRF-based and 3DGS-based methods in terms of quality and efficiency. The method is effective in handling large-scale urban scenes without relying on LiDAR or SfM points, making it suitable for real-world applications. HO-Gaussian reduces disk space usage by replacing spherical harmonics with Gaussian directional encoding and improves rendering quality through hybrid optimization of the grid-based volume and Gaussian pipeline. The method is evaluated on the Waymo and Argoverse datasets, demonstrating its effectiveness in synthesizing novel views with high-quality textures and consistent appearance across multiple cameras.HO-Gaussian is a hybrid optimization method for 3D Gaussian Splatting (3DGS) designed to enhance rendering in urban scenes. It addresses the limitations of traditional 3DGS methods that rely on Structure-from-Motion (SfM) points and struggle with rendering distant, low-texture, and sky areas. HO-Gaussian integrates a grid-based volume with the 3DGS pipeline, eliminating the need for SfM initialization and improving rendering quality through point densification and Gaussian positional and directional encoding. The method also introduces neural warping to ensure consistent rendering across multiple cameras, reducing overfitting to specific viewpoints. Key contributions include a novel pipeline for learning Gaussian positions from a grid-based volume, Gaussian positional and directional encoding to reduce disk space usage, and a neural warping scheme for multi-camera rendering. Experimental results on autonomous driving datasets show that HO-Gaussian achieves photo-realistic rendering in real-time, outperforming both NeRF-based and 3DGS-based methods in terms of quality and efficiency. The method is effective in handling large-scale urban scenes without relying on LiDAR or SfM points, making it suitable for real-world applications. HO-Gaussian reduces disk space usage by replacing spherical harmonics with Gaussian directional encoding and improves rendering quality through hybrid optimization of the grid-based volume and Gaussian pipeline. The method is evaluated on the Waymo and Argoverse datasets, demonstrating its effectiveness in synthesizing novel views with high-quality textures and consistent appearance across multiple cameras.