The paper introduces HO-Gaussian, a hybrid optimization method for 3D Gaussian Splatting (3DGS) in urban scenes. 3DGS has revolutionized neural rendering by enabling real-time production of high-quality renderings, but it relies on initial Structure-from-Motion (SfM) points and struggles with distant, sky, and low-texture areas. HO-Gaussian overcomes these limitations by combining a grid-based volume with the 3DGS pipeline, eliminating the need for SfM point initialization. It incorporates Point Densification to enhance rendering quality in problematic regions during training and introduces Gaussian Directional Encoding as an alternative to spherical harmonics, enabling view-dependent color representation. Additionally, neural warping is introduced to account for multi-camera systems, ensuring consistent object appearance across different cameras. Experimental results on autonomous driving datasets demonstrate that HO-Gaussian achieves photo-realistic rendering in real-time on multi-camera urban datasets, outperforming both NeRF-based and 3DGS-based methods in various evaluation metrics.The paper introduces HO-Gaussian, a hybrid optimization method for 3D Gaussian Splatting (3DGS) in urban scenes. 3DGS has revolutionized neural rendering by enabling real-time production of high-quality renderings, but it relies on initial Structure-from-Motion (SfM) points and struggles with distant, sky, and low-texture areas. HO-Gaussian overcomes these limitations by combining a grid-based volume with the 3DGS pipeline, eliminating the need for SfM point initialization. It incorporates Point Densification to enhance rendering quality in problematic regions during training and introduces Gaussian Directional Encoding as an alternative to spherical harmonics, enabling view-dependent color representation. Additionally, neural warping is introduced to account for multi-camera systems, ensuring consistent object appearance across different cameras. Experimental results on autonomous driving datasets demonstrate that HO-Gaussian achieves photo-realistic rendering in real-time on multi-camera urban datasets, outperforming both NeRF-based and 3DGS-based methods in various evaluation metrics.