  The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural
rendering, enabling real-time production of high-quality renderings. However,
the previous 3DGS-based methods have limitations in urban scenes due to
reliance on initial Structure-from-Motion(SfM) points and difficulties in
rendering distant, sky and low-texture areas. To overcome these challenges, we
propose a hybrid optimization method named HO-Gaussian, which combines a
grid-based volume with the 3DGS pipeline. HO-Gaussian eliminates the dependency
on SfM point initialization, allowing for rendering of urban scenes, and
incorporates the Point Densitification to enhance rendering quality in
problematic regions during training. Furthermore, we introduce Gaussian
Direction Encoding as an alternative for spherical harmonics in the rendering
pipeline, which enables view-dependent color representation. To account for
multi-camera systems, we introduce neural warping to enhance object consistency
across different cameras. Experimental results on widely used autonomous
driving datasets demonstrate that HO-Gaussian achieves photo-realistic
rendering in real-time on multi-camera urban datasets.
